Quasi noise-free digital holography

Digital holographic content manipulation for wide-angle holographic near-eye displays

In recent years, the development of holographic near-eye displays (HNED) has surpassed the progress of digital hologram recording systems, especially in terms of wide-angle viewing capabilities. Thus, there is capture-display parameters incompatibility, which makes it impossible to reconstruct recorded objects in wide-angle display. This paper presents a complete imaging chain extending the available content for wide-angle HNED of pupil and non-pupil configuration with narrow-angle digital holograms of real objects. To this end, a new framework based on the phase-space approach is proposed that includes a set of affine transformations required to account for all differences in capture-display cases. The developed method allows free manipulation of the geometry of reconstructed objects, including axial and lateral positioning and size scaling. At the same time, it has a low computational effort. The presented work is supported with non-paraxial formulas developed using the phase-space approach, enabling accurate tracing of the holographic signal, its reconstruction, and measuring appearing deformations. The applicability of the proposed hologram manipulation method is proven with experimental results of digital hologram reconstruction in wide-angle HNED.

High-efficiency static speckle-suppression method based on a combination of beam splitting cavity and liquid-core fiber

Current static speckle suppression methods have an extremely large system size and unsatisfactory performance. This study proposes a device called beam-splitting cavity (BSC) and establishes a model of speckle suppression based on the combination of BSC and a liquid-core fiber. Subsequently, a passive static speckle suppression system is constructed and the key factors affecting the speckle contrast are studied. Consequently, the speckle contrast was reduced from 30.2% to 3.1%, which is below the human-eye speckle-discrimination limit (<4%). The scheme consists entirely of passive optical elements, which are more applicable to projectors than the traditional static and dynamic speckle-suppression methods.

Noise suppression in the reconstructed image of digital holography based on the BEMDV method using improved particle swarm optimization

In digital holography, the speckle noise caused by the coherent nature of the light source and the light scattering generated by the light path system degrade the quality of the reconstructed image seriously. Therefore, in this paper, we propose what we believe to be is a novel noise reduction method combining bidimensional empirical mode decomposition (BEMD) with the variational method, termed BEMDV. The reconstructed image is first decomposed into a series of bidimensional intrinsic mode function (BIMF) components with different frequencies using the BEMD method, and then a certain number of BIMF components are selected for noise reduction by the variational method. An improved particle swarm optimization algorithm is adopted to optimize the key parameters of the proposed method, so as to further improve its noise reduction performance. A reflective off-axis digital holographic imaging system is used to collect the holograms of the coin and optical resolution plate, and the experimental research on noise reduction is carried out. The results with qualitative and quantitative analyses show that the proposed method achieves a better performance on noise reduction and detail preservation than other general methods, enormously enhancing the image quality of holographic reconstruction.

Digital holographic approaches to the detection and characterization of microplastics in water environments

Microplastic (MP) pollution is seriously threatening the environmental health of the world, which has accelerated the development of new identification and characterization methods. Digital holography (DH) is one of the emerging tools to detect MPs in a high-throughput flow. Here, we review advances in MP screening by DH. We examine the problem from both the hardware and software viewpoints. Automatic analysis based on smart DH processing is reported by highlighting the role played by artificial intelligence for classification and regression tasks. In this framework, the continuous development and availability in recent years of field-portable holographic flow cytometers for water monitoring also is discussed.

Review of computer-generated hologram algorithms for color dynamic holographic three-dimensional display

Holographic three-dimensional display is an important display technique because it can provide all depth information of a real or virtual scene without any special eyewear. In recent years, with the development of computer and optoelectronic technology, computer-generated holograms have attracted extensive attention and developed as the most promising method to realize holographic display. However, some bottlenecks still restrict the development of computer-generated holograms, such as heavy computation burden, low image quality, and the complicated system of color holographic display. To overcome these problems, numerous algorithms have been investigated with the aim of color dynamic holographic three-dimensional display. In this review, we will explain the essence of various computer-generated hologram algorithms and provide some insights for future research.

Deep learning in optical metrology: a review

With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional "physics-based" approach, deep-learning-enabled optical metrology is a kind of "data-driven" approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.

Deep learning speckle de-noising algorithms for coherent metrology: a review and a phase-shifted iterative scheme [Invited]

We present a review of deep learning algorithms dedicated to the processing of speckle noise in coherent imaging. We focus on methods that specifically process de-noising of input images. Four main classes of applications are described in this review: optical coherence tomography, synthetic aperture radar imaging, digital holography amplitude imaging, and fringe pattern analysis. We then present deep learning approaches recently developed in our group that rely on the retraining of residual convolutional neural network structures to process decorrelation phase noise. The paper ends with the presentation of a new approach that uses an iterative scheme controlled by an input SNR estimator associated with a phase-shifting procedure.

Gaze-Contingent Retinal Speckle Suppression for Perceptually-Matched Foveated Holographic Displays

Computer-generated holographic (CGH) displays show great potential and are emerging as the next-generation displays for augmented and virtual reality, and automotive heads-up displays. One of the critical problems harming the wide adoption of such displays is the presence of speckle noise inherent to holography, that compromises its quality by introducing perceptible artifacts. Although speckle noise suppression has been an active research area, the previous works have not considered the perceptual characteristics of the Human Visual System (HVS), which receives the final displayed imagery. However, it is well studied that the sensitivity of the HVS is not uniform across the visual field, which has led to gaze-contingent rendering schemes for maximizing the perceptual quality in various computer-generated imagery. Inspired by this, we present the first method that reduces the "perceived speckle noise" by integrating foveal and peripheral vision characteristics of the HVS, along with the retinal point spread function, into the phase hologram computation. Specifically, we introduce the anatomical and statistical retinal receptor distribution into our computational hologram optimization, which places a higher priority on reducing the perceived foveal speckle noise while being adaptable to any individual's optical aberration on the retina. Our method demonstrates superior perceptual quality on our emulated holographic display. Our evaluations with objective measurements and subjective studies demonstrate a significant reduction of the human perceived noise.

Modeling of speckle decorrelation in digital Fresnel holographic interferometry

This paper presents analytical modeling of the speckle decorrelation noise in digital Fresnel holographic interferometry. The theoretical analysis is carried out by considering the complex coherence factor between two speckled images from two digitally reconstructed holograms at two different instants. The expression giving the modulus of the coherence factor is established and depends on the local surface deformation and parameters from the holographic setup. The analysis is supported by realistic simulations and experiments. Both simulations and experimental results exhibit a very good agreement with the theoretical prediction.

Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images

In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.

Speckle-reduced reconstruction of a single-shot hologram by multiple tip-tilt modulations

Speckle can be attenuated by averaging the reconstructed images of each sub-hologram or being filtered with different filters, at the expense of resolution. We propose a de-speckling method for a single-shot digital hologram while maintaining the resolution. Different tip-tilt phases are demonstrated to cause changes only for the speckle distributions of the reconstructed image. The speckle is attenuated by averaging these intensity images with different speckle distributions. The normalized contrast can be reduced to 0.56 by averaging only 20 different reconstructed images. When the averaged image is processed with block matching and 3D filtering, a further de-speckled image at a normalized contrast of 0.46 can be obtained with highly preserved resolution.

Speckle reduction in digital holography with low-dimensional reconstruction

Speckle reduction is a crucial technique since the presence of speckle disturbs the quality of the reconstruction in digital holography. In this paper, we present an easy, fast, and efficient single-shot method to reduce speckle noise in digital holography. The method reconstructs subholograms from a single hologram. Then, sub-reconstruction images are randomly shuffled and divided into several groups and low-dimensional noise-reduced images can be achieved by averaging sub-reconstruction image groups by groups. Next, these low-dimensional noise-reduced images are combined to obtain a noise-reduced image. Finally, the noise-reduced image is processed by a mean filter to obtain a final image, which has substantially less speckle noise while preserving the dimensions of the original image. The experimental results demonstrate the effectiveness of the proposed method and indicate its potential in real-time digital holography.

Physical pupil manipulation for speckle reduction in digital holographic microscopy

The reduction of speckle noise by physically changing the pupil of the imaging system, as first envisioned in optical holography, is experimentally applied to a digital holographic microscope (DHM). The imaging pupil of a DHM, operating in image plane telecentric-afocal architecture, is changed in a controlled way between successive recordings, allowing the shooting of multiple partially-decorrelated holograms. Averaging the numerically reconstructed holograms yields amplitude and/or phase images with reduced speckle noise. Experimental results of biological specimens and a phase-only resolution test show the feasibility to recover micron-sized features in images with reduced speckle noise.

Speckle Noise Reduction in Digital Holography Using a DMD and Multi-Hologram Resampling

Speckle noise is a well-documented problem on coherent imaging techniques like Digital Holography. A method to reduce the speckle noise level is presented, based on introducing a Digital Micromirror Device to phase modulate the illumination over the object. Multiple holograms with varying illuminations are recorded and the reconstructed intensities are averaged to obtain a final improved image. A simple numerical resampling scheme is proposed to further improve noise reduction. The obtained results demonstrate the effectiveness of the hybrid approach.

Smart filtering of phase residues in noisy wrapped holograms

Phase unwrapping is one of the major challenges in multiple branches of science that extract three-dimensional information of objects from wrapped signals. In several applications, it is important to extract the unwrapped information with minimal signal resolution degradation. However, most of the denoising techniques for unwrapping are designed to operate on the entire phase map to remove a limited number of phase residues, and therefore they significantly degrade critical information contained in the image. In this paper, we present a novel, smart, and automatic filtering technique for locally minimizing the number of phase residues in noisy wrapped holograms, based on the phasor average filtering (PAF) of patches around each residue point. Both patch sizes and PAF filters are increased in an iterative algorithm to minimize the number of residues and locally restrict the artifacts caused by filtering to the pixels around the residue pixels. Then, the improved wrapped phase can be unwrapped using a simple phase unwrapping technique. The feasibility of our method is confirmed by filtering, unwrapping, and enhancing the quality of a noisy hologram of neurons; the intensity distribution of the spatial frequencies demonstrates a 40-fold improvement, with respect to previous techniques, in preserving the higher frequencies.

Polychromatic digital holographic microscopy: a quasicoherent-noise-free imaging technique to explore the connectivity of living neuronal networks

Significance: Over the past decade, laser-based digital holographic microscopy (DHM), an important approach in the field of quantitative-phase imaging techniques, has become a significant label-free modality for live-cell imaging and used particularly in cellular neuroscience. However, coherent noise remains a major drawback for DHM, significantly limiting the possibility to visualize neuronal processes and precluding important studies on neuronal connectivity. Aim: The goal is to develop a DHM technique able to sharply visualize thin neuronal processes. Approach: By combining a wavelength-tunable light source with the advantages of hologram numerical reconstruction of DHM, an approach called polychromatic DHM (P-DHM), providing OPD images with drastically decreased coherent noise, was developed. Results: When applied to cultured neuronal networks with an air microscope objective ( 20 × , 0.8 NA), P-DHM shows a coherent noise level typically corresponding to 1 nm at the single-pixel scale, in agreement with the 1 / N -law, allowing to readily visualize the 1 - μ m -wide thin neuronal processes with a signal-to-noise ratio of ∼ 5 . Conclusions: Therefore, P-DHM represents a very promising label-free technique to study neuronal connectivity and its development, including neurite outgrowth, elongation, and branching.

Deep Learning-Based Wrapped Phase Denoising Method for Application in Digital Holographic Speckle Pattern Interferometry

This paper presents a new processing method for denoising interferograms obtained by digital holographic speckle pattern interferometry (DHSPI) to serve in the structural diagnosis of artworks. DHSPI is a non-destructive and non-contact imaging method that has been successfully applied to the structural diagnosis of artworks by detecting hidden subsurface defects and quantifying the deformation directly from the surface illuminated by coherent light. The spatial information of structural defects is mostly delivered as local distortions interrupting the smooth distribution of intensity during the phase-shifted formation of fringe patterns. Distortions in fringe patterns are recorded and observed from the estimated wrapped phase map, but the inevitable electronic speckle noise directly affects the quality of the image and consequently the assessment of defects. An effective method for denoising DHSPI wrapped phase based on deep learning is presented in this paper. Although a related method applied to interferometry for reducing Gaussian noise has been introduced, it is not suitable for application in DHSPI to reduce speckle noise. Thus, the paper proposes a new method to remove speckle noise in the wrapped phase. Simulated data and experimental captured data from samples prove that the proposed method can effectively reduce the speckle noise of the DHSPI wrapped phase to extract the desired information. The proposed method is helpful for accurately detecting defects in complex defect topography maps and may help to accelerate defect detection and characterization procedures.

Single-shot speckle reduction by elimination of redundant speckle patterns in digital holography

Speckle reduction is a crucial technique, since the presence of speckle disturbs the quality of the reconstruction in digital holography. In this paper, we present a redundant speckle elimination method to suppress the speckle noise. For the same position in each of the reconstructed sub-images, we consider pixels with the same gray value as information with the same speckle distribution. Therefore, a speckle-suppressed gray value can be obtained by extracting pixels with different gray values and then averaging. Through theoretical analysis and experiments, we demonstrate that speckle contrast can be decreased significantly by using the proposed method. Moreover, we show that the despeckle strength of the proposed method highly depends on the number of binary masks. These results indicate the potential of the proposed method for various applications.

Numerically Enhanced Stimulated Emission Depletion Microscopy with Adaptive Optics for Deep-Tissue Super-Resolved Imaging

In stimulated emission depletion (STED) nanoscopy, the major origin of decreased signal-to-noise ratio within images can be attributed to sample photobleaching and strong optical aberrations. This is due to STED utilizing a high-power depletion laser (increasing the risk of photodamage), while the depletion beam is very sensitive to sample-induced aberrations. Here, we demonstrate a custom-built STED microscope with automated aberration correction that is capable of 3D super-resolution imaging through thick, highly aberrating tissue. We introduce and investigate a state of the art image denoising method by block-matching and collaborative 3D filtering (BM3D) to numerically enhance fine object details otherwise mixed with noise and further enhance the image quality. Numerical denoising provides an increase in the final effective resolution of the STED imaging of 31% using the well established Fourier ring correlation metric. Results achieved through the combination of aberration correction and tailored image processing are experimentally validated through super-resolved 3D imaging of axons in differentiated induced pluripotent stem cells growing under an 80 μm thick layer of tissue with lateral and axial resolution of 204 and 310 nm, respectively.

Fast and accurate sCMOS noise correction for fluorescence microscopy

The rapid development of scientific CMOS (sCMOS) technology has greatly advanced optical microscopy for biomedical research with superior sensitivity, resolution, field-of-view, and frame rates. However, for sCMOS sensors, the parallel charge-voltage conversion and different responsivity at each pixel induces extra readout and pattern noise compared to charge-coupled devices (CCD) and electron-multiplying CCD (EM-CCD) sensors. This can produce artifacts, deteriorate imaging capability, and hinder quantification of fluorescent signals, thereby compromising strategies to reduce photo-damage to live samples. Here, we propose a content-adaptive algorithm for the automatic correction of sCMOS-related noise (ACsN) for fluorescence microscopy. ACsN combines camera physics and layered sparse filtering to significantly reduce the most relevant noise sources in a sCMOS sensor while preserving the fine details of the signal. The method improves the camera performance, enabling fast, low-light and quantitative optical microscopy with video-rate denoising for a broad range of imaging conditions and modalities.

Scientific complementary metal-oxide semiconductor (sCMOS) cameras have advanced the imaging field, but they often suffer from additional noise compared to CCD sensors. Here the authors present a content-adaptive algorithm for the automatic correction of sCMOS-related noise for fluorescence microscopy.

Hybrid method for speckle noise reduction in digital holography

In digital holography, the inherited speckle noise degrades imaging quality due to the coherent laser source. To overcome this problem, a hybrid method for speckle noise reduction is presented by combining a novel angular diversity approach with the block-matching and 3D filtering (BM3D) algorithm. A serial of holograms is first captured by the proposed recording approach, and then the image with high signal-to-noise ratio is obtained by averaging multiple reconstructed intensity images. Finally, the residual noise in the averaged image is further eliminated by the BM3D filtering algorithm. The speckle noise is significantly suppressed, and a nearly speckle-free image can be obtained. Experimental results demonstrate the effectiveness of the proposed method.

Phase-error correction in digital holography using single-shot data

In remote-sensing applications, digital holography data often includes both phase errors from atmospheric turbulence and fully developed laser speckle from rough objects. When processing single-shot data, i.e., data from a single hologram, the high speckle contrast makes it more difficult to correct for atmospheric phase errors compared to scenarios where multiple speckle realizations are available for processing. A Bayesian phase-error correction algorithm [J. Opt. Soc. Am. A34, 1659 (2017)JOAOD60740-323210.1364/JOSAA.34.001659] was recently developed for use with single-shot data. The features of this approach are discussed and used to implement an alternative algorithm based on image-sharpness maximization. Algorithm performance is tested using simulated data for a range of signal-to-noise ratios (SNRs) and turbulence conditions. Using a combination of appropriate parameterization of the phase-error estimates and spatial binning for speckle-contrast reduction, the image-sharpness algorithm achieves performance comparable (better in the high-SNR regime but worse in the low-SNR regime) to the Bayesian approach. Limited experimental results are also presented.

Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography

We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concluded by applying our technique to reduce the temporally changing noise emerging from focal drift in time-lapse imaging of biological cells. This reduction cannot be performed using other optical methods for denoising.

Comparative study of multi-look processing for phase map de-noising in digital Fresnel holographic interferometry

This paper presents a comparative study of multi-look approaches for de-noising phase maps from digital holographic interferometry. A database of 160 simulated phase fringe patterns with eight different phase fringe patterns with fringe diversity was computed. For each fringe pattern, 20 realistic noise realizations are generated in order to simulate a multi-look process with 20 inputs. A set of 22 de-noising algorithms was selected and processed for each simulation. Three approaches for multi-look processing are evaluated. Quantitative appraisal is obtained using two metrics. The results show good agreement for algorithm rankings obtained with both metrics. One singular and highly practical result of the study is that a multi-look approach with average looks before noise processing performs better than averaging computed with all de-noised looks. The results also demonstrate that the two-dimensional windowed Fourier transform filtering exhibits the best performance in all cases and that the block-matching 3D (BM3D) algorithm is second in the ranking.

Dictionary Learning Phase Retrieval from Noisy Diffraction Patterns

This paper proposes a novel algorithm for image phase retrieval, i.e., for recovering complex-valued images from the amplitudes of noisy linear combinations (often the Fourier transform) of the sought complex images. The algorithm is developed using the alternating projection framework and is aimed to obtain high performance for heavily noisy (Poissonian or Gaussian) observations. The estimation of the target images is reformulated as a sparse regression, often termed sparse coding, in the complex domain. This is accomplished by learning a complex domain dictionary from the data it represents via matrix factorization with sparsity constraints on the code (i.e., the regression coefficients). Our algorithm, termed dictionary learning phase retrieval (DLPR), jointly learns the referred to dictionary and reconstructs the unknown target image. The effectiveness of DLPR is illustrated through experiments conducted on complex images, simulated and real, where it shows noticeable advantages over the state-of-the-art competitors.

Ritchey-Common sparse-aperture testing of the Giant Steerable Science Mirror

The Giant Steerable Science Mirror (GSSM) is the tertiary mirror of the future large telescope, the Thirty Meter Telescope. However, the mirror is too large to be tested using only one aperture, and using many apertures will increase the cost of testing. To accomplish testing at a low cost, the number of apertures should be reduced. The Ritchey-Common (R-C) testing method, commonly used for testing large flat surfaces, uses only a reference spherical mirror and avoids the use of large planar interferometers. Additionally, only the low-spatial-frequency mirror figure is relevant in the system assembly and alignment. Hence, the applicability of sparse-aperture testing is investigated in this paper. Sparse-aperture testing and the R-C method were combined to lower the cost. Using this method and the normalized point source sensitivity (PSSn), the mirror figure can be specified in a simple and accurate manner. Moreover, as fewer subapertures are under test, the efficiency can be improved. An error analysis is conducted, focusing on the shifting error, irregularity error, tipping error, tangential/sagittal error, and seeing. For the testing of the GSSM prototype, the error analysis showed the total error in PSSn is 0.9701.

Adaptive dual-exposure fusion-based transport of intensity phase microscopy

Via the transport of intensity phase microscopy, quantitative phase can be retrieved directly from captured multi-focal intensities. The accuracy of the retrieved phases depends highly on the quality of the recorded images; therefore, the exposure time should be carefully chosen for high-quality intensity captures. However, it is difficult to record well-exposure intensities to maintain rather a high signal to noise ratio and to avoid over-exposure due to the complex samples. In order to simplify the exposure determination, here the adaptive dual-exposure fusion-based transport of intensity phase microscopy is proposed: with captured short- and long-exposure images, the well-exposure multi-focal images can be numerically reconstructed, and then high-accurate phase can be computed from these reconstructed intensities. With both simulations and experiments provided in this paper, it is proved that the adaptive dual-exposure fusion-based transport of intensity phase microscopy not only provides numerically reconstructed well-exposure image with simple operation and fast speed but also extracts highly accurate retrieved phase. Moreover, the exposure time selection scope of the proposed method is much wider than that based on single exposure, and even though there is an over-exposure region in the long-exposure image, a well-exposure image can still be reconstructed with high precision. Considering its advantages of high accuracy, fast speed, simple operation, and wide application scope, the proposed technique can be adopted as quantitative phase microscopy for high-quality observations and measurements.

Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks

In this Letter, we propose a fast speckle noise reduction method with only a single reconstructed image based on convolutional neural networks. The proposed network has multi-sized kernels that can capture the speckle noise component effectively from digital holographic images. For robust noise reduction performance, the network is trained with a large noisy image dataset that has object-dependent noise and a wide range of noise levels. The experimental results show the fast, robust, and outstanding speckle noise reduction performance of the proposed approach.

Strategies for reducing speckle noise in digital holography

Digital holography (DH) has emerged as one of the most effective coherent imaging technologies. The technological developments of digital sensors and optical elements have made DH the primary approach in several research fields, from quantitative phase imaging to optical metrology and 3D display technologies, to name a few. Like many other digital imaging techniques, DH must cope with the issue of speckle artifacts, due to the coherent nature of the required light sources. Despite the complexity of the recently proposed de-speckling methods, many have not yet attained the required level of effectiveness. That is, a universal denoising strategy for completely suppressing holographic noise has not yet been established. Thus the removal of speckle noise from holographic images represents a bottleneck for the entire optics and photonics scientific community. This review article provides a broad discussion about the noise issue in DH, with the aim of covering the best-performing noise reduction approaches that have been proposed so far. Quantitative comparisons among these approaches will be presented.

Quantitative phase imaging system with slightly-off-axis configuration and suitable for objects both larger and smaller than the size of the image sensor

We propose a quantitative phase imaging system with exact slightly-off-axis configuration and suitable for objects both smaller and larger than the size of the effective recording region of the image sensors. In this system, the object is illuminated by a convergent spherical beam and a specially designed aperture filter is placed on the spatial frequency plane of the object wave; at the same time, a point source emitting from the edge of the aperture is taken as the reference beam, so that the optimal frequency condition for reconstruction of slightly-off-axis digital holograms can be always guaranteed for both large and small objects as well as different magnification (or the field of view) configurations. At the same time, a 1x2 single-mode optical fiber splitter is used for generating the reference and the illumination beams. Benefited from such fiber-based slightly-off-axis design, the proposed system provides a low-cost way to convert a regular microscope into a slightly-off-axis holographic one for microbiological specimens with a high spatial resolution.

Variable aperture-based ptychographical iterative engine method

A variable aperture-based ptychographical iterative engine (vaPIE) is demonstrated both numerically and experimentally to reconstruct the sample phase and amplitude rapidly. By adjusting the size of a tiny aperture under the illumination of a parallel light beam to change the illumination on the sample step by step and recording the corresponding diffraction patterns sequentially, both the sample phase and amplitude can be faithfully reconstructed with a modified ptychographical iterative engine (PIE) algorithm. Since many fewer diffraction patterns are required than in common PIE and the shape, the size, and the position of the aperture need not to be known exactly, this proposed vaPIE method remarkably reduces the data acquisition time and makes PIE less dependent on the mechanical accuracy of the translation stage; therefore, the proposed technique can be potentially applied for various scientific researches.

Coherent noise reduction in digital holographic microscopy by averaging multiple holograms recorded with a multimode laser

In digital holographic microscopy (DHM), it is undesirable to observe coherent noise in the reconstructed images. The sources of the noise are mainly the parasitic interference fringes caused by multiple reflections and the speckle pattern caused by the optical scattering on the object surface. Here we propose a noise reduction approach in DHM by averaging multiple holograms recorded with a multimode laser. Based on the periodicity of the temporal coherence of a multimode semiconductor laser, we acquire a series of holograms by changing the optical path length difference between the reference beam and object beam. Because of the use of low coherence light, we can remove the parasitic interference fringes caused by multiple reflections in the holograms. In addition, the coherent noise patterns change in this process due to the different optical paths. Therefore, the coherent noise can be reduced by averaging the multiple reconstructions with uncorrelated noise patterns. Several experiments have been carried out to validate the effectiveness of the proposed approach for coherent noise reduction in DHM. It is shown a remarkable improvement both in amplitude imaging quality and phase measurement accuracy.

Improved generation method utilizing a modified Fourier spectrum for Airy beams with the phase-only filter technique

We present an improved method to generate Airy beams utilizing a liquid crystal on silicon (LCoS) device. In this method, the phase and amplitude information of a modified Fourier spectrum of an Airy beam together with a Fresnel holographic lens is encoded onto the LCoS using the phase-only filter technique; thus, a desired Airy beam is formed in the focal plane of the Fresnel holographic lens. In this paper, the principle of the proposed method is described in detail, and both the excellent numerical simulations and experimental results for verifying this method are demonstrated. It is shown that the new generation method is accurate and simple; in particular, the setup is more compact compared to the conventional Fourier transform method, which comprises only the input polarized laser and a LCoS device. This effective method will further promote investigations into the properties and applications of Airy beams.

Compact single-shot four-wavelength quantitative phase microscopy with polarization- and frequency-division demultiplexing

We present a novel single-shot four-wavelength quantitative phase microscopy (FW-QPM). Four lasers operating at different wavelengths are multiplexed with a pair of dichroic mirrors and a polarization beam splitter in a three-mirror quasi-common-path interferometer. After a single-shot interference pattern is obtained with a monochrome camera, four holograms of different wavelengths were demultiplexed from it in the frequency domain with polarization- and frequency-division multiplexing. Polarization-division demultiplexing scheme uses polarization dependent visibility changes in an interference pattern, and it plays a critical role in making only two interference patterns exist within a single quadrant in the frequency domain. We have used a single-mode optical fiber as a phase object sample and demonstrated that a measured single-shot interference pattern can be successfully demultiplexed into four different interferograms of different wavelengths with our proposed scheme.

Ultra-high speed digital micro-mirror device based ptychographic iterative engine method

To reduce the long data acquisition time of the common mechanical scanning based Ptychographic Iterative Engine (PIE) technique, the digital micro-mirror device (DMD) is used to form the fast scanning illumination on the sample. Since the transverse mechanical scanning in the common PIE is replaced by the on/off switching of the micro-mirrors, the data acquisition time can be reduced from more than 15 minutes to less than 20 seconds for recording 12 × 10 diffraction patterns to cover the same field of 147.08 mm2. Furthermore, since the precision of DMD fabricated with the optical lithography is always higher than 10 nm (1 μm for the mechanical translation stage), the time consuming position-error-correction procedure is not required in the iterative reconstruction. These two improvements fundamentally speed up both the data acquisition and the reconstruction procedures in PIE, and relax its requirements on the stability of the imaging system, therefore remarkably improve its applicability for many practices. It is demonstrated experimentally with both USAF resolution target and biological sample that, the spatial resolution of 5.52 μm and the field of view of 147.08 mm2 can be reached with the DMD based PIE method. In a word, by using the DMD to replace the translation stage, we can effectively overcome the main shortcomings of common PIE related to the mechanical scanning, while keeping its advantages on both the high resolution and large field of view.

Quantitative study on a resampling mask method for speckle reduction with amplitude superposition

One-shot digital holographic imaging has the advantages of high stability and low temporal cost. However, its reconstruction is degraded severely by the laser speckle. A rectangle, ellipse, and diamond resampling mask method in spatial domain for speckle reduction is proposed. The effectiveness of the method for speckle reduction is explained successfully. In the method, one hologram recorded in a certain size is divided into N=S×T sub-holograms. Angular spectrum transform is applied to the holographic reconstruction of a diffuse object. N reconstructed amplitude images are calculated from the corresponding sub-holograms. Benefitting from speckle's random distribution, superimposing these N uncorrelated amplitude images would lead to a final reconstructed image with reduced speckle. Normalized relative standard deviation values of the reconstructed image are in good agreement with the asymptotical law. The maximum relative errors between the experiment data and the theoretical values are below 7.2%. The effect of the method on the spatial resolution of the reconstructed image is also quantitatively evaluated. Experimental and simulation results prove the feasibility and effectiveness of the proposed method.

Generation of coherent and incoherent Airy beam arrays and experimental comparisons of their scintillation characteristics in atmospheric turbulence

We investigate, through numerical calculation and experiments, the incoherent combination of a 2D Airy beam array (ICCAB) and the coherent combination of a 2D Airy beam array (CCAB), respectively. Excellent experimental results are obtained for both ICCAB and CCAB. The on-axis scintillation indices of ICCAB and CCAB at the receiver plane in atmospheric turbulence are also compared experimentally. It is shown that ICCAB has a smaller scintillation index than that of CCAB in the same turbulent condition due to the coherence reduction of the constituent beamlets. The results obtained in this paper are useful for the development of beam propagation through atmosphere turbulence.

Reduction of speckle noise in holographic images using spatial jittering in numerical reconstructions

This Letter presents an easy, fast, and efficient single-shot method to reduce speckle noise in digital Fresnel holography. In this method, several images from a single hologram are reconstructed by introducing spatial displacements in the Fresnel kernel. Spatial jitters produce images with different speckle positions. Averaging the set of numerically produced images leads to a strong reduction of speckle noise in both amplitude and phase difference images. The experimental results show the suitability of the proposed approach and confirm its applicability to digital holographic interferometry.

Rapid in-focus corrections on quantitative amplitude and phase imaging using transport of intensity equation method

Transport of intensity equation (TIE) method can acquire sample phase distributions with high speed and accuracy, offering another perspective for cellular observations and measurements. However, caused by incorrect focal plane determination, blurs and halos are induced, decreasing resolution and accuracy in both retrieved amplitude and phase information. In order to obtain high-accurate sample details, we propose TIE based in-focus correction technique for quantitative amplitude and phase imaging, which can locate focal plane and then retrieve both in-focus intensity and phase distributions combining with numerical wavefront extraction and propagation as well as physical image recorder translation. Certified by both numerical simulations and practical measurements, it is believed the proposed method not only captures high-accurate in-focus sample information, but also provides a potential way for fast autofocusing in microscopic system.

Smartphone based hand-held quantitative phase microscope using the transport of intensity equation method

In order to realize high contrast imaging with portable devices for potential mobile healthcare, we demonstrate a hand-held smartphone based quantitative phase microscope using the transport of intensity equation method. With a cost-effective illumination source and compact microscope system, multi-focal images of samples can be captured by the smartphone's camera via manual focusing. Phase retrieval is performed using a self-developed Android application, which calculates sample phases from multi-plane intensities via solving the Poisson equation. We test the portable microscope using a random phase plate with known phases, and to further demonstrate its performance, a red blood cell smear, a Pap smear and monocot root and broad bean epidermis sections are also successfully imaged. Considering its advantages as an accurate, high-contrast, cost-effective and field-portable device, the smartphone based hand-held quantitative phase microscope is a promising tool which can be adopted in the future in remote healthcare and medical diagnosis.

On-speckle suppression in IR digital holography

Long-IR wavelength is the best option for capturing digital holograms of large-size, real-world objects. However, the coherent noise level in a long-IR hologram is by far larger than that of a visible wavelength recording, thus resulting in a poor quality of both numerical and optical reconstructions. In this Letter, we show how such coherent noise can be efficiently suppressed by employing an optical scanning multi-look approach, in combination with 3D block matching numerical filtering. Results demonstrate the possibility to obtain near noise-free numerical reconstructions of IR digital holograms of large-size objects, while preserving resolution. We applied this method to the holograms of a rotating statuette. It will be shown that a remarkable contrast enhancement is achievable along with the recovery of object details that otherwise would be lost because of large speckle grains intrinsically due to the source coherence.