Speckle denoising in digital holography by nonlocal means filtering

Holographic image denoising for dense droplet field using conditional diffusion model

The Letter delves into an approach to holographic image denoising, drawing inspiration from the generative paradigm. It introduces a conditional diffusion model framework that effectively suppresses twin-image noises and speckle noises in dense particle fields with a large depth of field (DOF). Specific training and inference configurations are meticulously outlined. For evaluation, the method is tested using calibration dot board data and droplet field data, encompassing gel atomization captured via inline holography and aviation kerosene swirl spray through off-axis holography. The performance is assessed using three distinct metrics. The metric outcomes, along with representative examples, robustly demonstrate its superior noise reduction, detail preservation, and generalization capabilities when compared to two other methods. The proposed method not only pioneers the field of generative holographic image denoising but also highlights its potential for industrial applications, given its reduced dependency on high-quality training labels.

Digital in-line holographic microscopy for label-free identification and tracking of biological cells

Digital in-line holographic microscopy (DIHM) is a non-invasive, real-time, label-free technique that captures three-dimensional (3D) positional, orientational, and morphological information from digital holographic images of living biological cells. Unlike conventional microscopies, the DIHM technique enables precise measurements of dynamic behaviors exhibited by living cells within a 3D volume. This review outlines the fundamental principles and comprehensive digital image processing procedures employed in DIHM-based cell tracking methods. In addition, recent applications of DIHM technique for label-free identification and digital tracking of various motile biological cells, including human blood cells, spermatozoa, diseased cells, and unicellular microorganisms, are thoroughly examined. Leveraging artificial intelligence has significantly enhanced both the speed and accuracy of digital image processing for cell tracking and identification. The quantitative data on cell morphology and dynamics captured by DIHM can effectively elucidate the underlying mechanisms governing various microbial behaviors and contribute to the accumulation of diagnostic databases and the development of clinical treatments.

Brain tumor grading diagnosis using transfer learning based on optical coherence tomography

In neurosurgery, accurately identifying brain tumor tissue is vital for reducing recurrence. Current imaging techniques have limitations, prompting the exploration of alternative methods. This study validated a binary hierarchical classification of brain tissues: normal tissue, primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and low-grade glioma (LGG) using transfer learning. Tumor specimens were measured with optical coherence tomography (OCT), and a MobileNetV2 pre-trained model was employed for classification. Surgeons could optimize predictions based on experience. The model showed robust classification and promising clinical value. A dynamic t-SNE visualized its performance, offering a new approach to neurosurgical decision-making regarding brain tumors.

Speckle suppression in holographic phase fringe patterns with different level noises based on FFDNet

In this paper, an ANLVENet speckle suppression method in holographic phase fringe patterns with different level noises is proposed based on FFDNet, combined with asymmetric pyramid non-local block with a verge extraction module. The experimental results are compared to three network models and several representative algorithms. It is shown that the ANLVENet method not only has better superiority in the speckle suppression with different noise levels, but also preserves more details of the image edge. In addition, another speckle noise model is applied in the phase fringe patterns to prove the stronger generalization of the ANLVENet algorithm. The proposed method is suitable for suppressing the speckle with different levels in a large noise range under complex environmental conditions.

Nonlocal means filter based on cosine similarity applied in speckle reduction of digital holography

In the field of digital holography, the speckle caused by coherent light greatly disturbs the quality of the reconstruction. This paper presents an innovative method to efficiently reduce speckle noise with a nonlocal means filter based on cosine similarity that determines the weight of each traversal pixel to the target pixel by comparing the similarity between the target pixel neighborhood and the traversal pixel neighborhood. Experimental results with qualitative and quantitative analysis indicate that the proposed method significantly improves noise reduction performance while preserving the details of the original image. Compared with other general image-processing methods, this well-directed method is more in line with the characteristics of holographic speckle noise and has obvious advantages in various metrics.

Uneven wrapped phase pattern denoising using a deep neural network

The wrapped phase patterns obtained from an object composed of different materials have uneven gray values. In this paper, we improve the dilated-blocks-based deep convolution neural network (DBDNet) and build a new dataset for restoring the uneven gray values of uneven wrapped phase patterns as well as eliminating the speckle noise. In our method, we improve the structure of dilated blocks in DBDNet to enhance the ability of obtaining full scales of gray values and speckle noise information in the uneven phase patterns. We use the combined MS_SSIM+L1 loss function to improve the denoising and restoration performance of our method. We compare three representative networks ResNet-based, ADNet, and BRDNet in denoising with our proposed method. We test the three compared methods and our method on one group of computer-simulated and one group of experimentally obtained uneven noisy wrapped phase patterns from a dynamic measurement. We also conduct the ablation experiments on the improved model structure and the combined loss function used in our method. The denoising performance has been evaluated quantitatively and qualitatively. The denoising results demonstrate that our proposed method can reduce high speckle noise, restore the uneven gray values of wrapped phase patterns, and get better results than the compared methods.

Reduction of speckle noise in digital holography using a neighborhood filter based on multiple sub-reconstructed images

The application of digital holography in several fields is limited since speckle destroys the original information of the reconstructed image. This paper proposes a neighborhood filter based on multiple sub-reconstructed images according to the random distribution of speckle noise. In this method, the denoised value is equal to the weighted sum of neighboring pixel values, and the weight is calculated by the degree of correlation between different positions of multiple sub-holograms. The experimental results show that the method can greatly reduce the speckle noise, and its noise reduction performance is superior to traditional digital image processing algorithms.

Recording of Long Low-Amplitude Bulk Elastic Waves in Transparent Solid Waveguides by Digital and Classical Holography

In this paper we compare two implementations of the holographic technique for recording long, nonlinear, elastic waves of low amplitude in solid polymer waveguides: classical holographic interferometry and digital holography. Both implementations are realized in transmission configuration, with recording in the off-axis schematic. The advantages and disadvantages of these implementations are discussed as applied to the investigation of the evolution of shock waves and strain solitons in transparent solid waveguides.

Speckle reduction in digital holography with non-local means filter based on the Pearson correlation coefficient and Butterworth filter

This Letter presents a non-local means filter based on the Pearson correlation coefficient and Butterworth high-pass filter. In the method, the new gray value of the denoising pixel is equal to the weighted sum of the surrounding pixel values. We use the Pearson correlation coefficient between the pixels to calculate the weight of the surrounding pixels to the denoising pixel, then use Butterworth high-pass filter to optimize. Experimental results show that the method effectively reduces the speckle noise of digital holography and the image details are also very rich. At the same time, its performance is still better when compared with methods such as BM3D.

Dynamic displacement measurement in digital holographic interferometry using eigenspace analysis

Non-contact measurement of displacement undergone by a deformed object is an important application problem in digital holographic interferometry. Such measurements usually demand reliable estimation of interference phase even in the presence of severe noise. This article describes a method for non-contact displacement testing by investigating a robust phase retrieval approach in digital holographic interferometry. The approach is based on eigenspace processing of the complex interference field signal in digital holographic interferometry. The performance of the proposed method for phase retrieval under severe noise conditions is illustrated using simulation results. The practical utility of the proposed method is demonstrated for dynamic deformation analysis using experimental data from digital holographic interferometry.

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.

Improving the intensity-contrast image of a noisy digital hologram by convolution of Chebyshev type 2 and elliptic filters

In this paper, a new, to the best of our knowledge, technique convolves the windowed Fourier filtering (WFF) of the Fresnel transform with the transfer functions of both Chebyshev type 2 and elliptic filters to enhance the intensity-contrast image of a noisy digital hologram. The recorded digital hologram is reconstructed by the Fresnel approach, the reconstructed intensity-contrast image is transformed by WFF, and the obtained spectrum is convolved in frequency domain with the transfer functions of Chebyshev type 2 and elliptic filters. The result of convolution is transformed by inverse WFF to produce a speckle-free image with a sharp roll-off and no ripples in both pass- and stop-bands. The experimental results with a die in the presence and absence of a rotating ground glass diffuser are shown and demonstrate that the resolution can be effectively enhanced with simple setup and procedure. The proposed technique can improve the capabilities of digital holography in three-dimensional (3D) microscopy.

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.

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.

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.

Autofocusing and image fusion for multi-focus plankton imaging by digital holographic microscopy

Digital holographic microscopy is becoming increasingly useful for the analysis of marine plankton. In this study, we investigate autofocusing and image fusion in digital holographic microscopy. We propose an area metric autofocusing method and an improved wavelet-based image fusion method. In the area metric autofocusing method, a hologram image is initially segmented into several plankton regions for focus plane detection, and an area metric is then applied to these regions. In the improved wavelet-based image fusion method, a marked map is introduced for labeling each plankton region with the order of refocus plane images that accounts for the most pixels. The results indicate that the area metric autofocusing method applied to each plankton region provides a higher depth resolution accuracy than a number of general autofocusing methods, and the mean accuracy increases by approximately 33%. The improved wavelet-based image fusion method can fuse more than nine reconstructed plane images at a time and effectively eliminate fringes and speckle noise, and the fused image is much clearer than that of a general wavelet-based method, a sparse decomposition method, and a pulse-coupled neural networks method. This work has practical value for plankton imaging using digital holographic microscopy.

Assessment of speckle denoising filters for digital holography using subjective and objective evaluation models

Digital holography is an emerging imaging technique for displaying and sensing three-dimensional objects. The perceived image quality of a hologram is frequently corrupted by speckle noise due to coherent illumination. Although several speckle noise reduction methods have been developed so far, there are scarce quality assessment studies to address their performance, and they typically focus solely on objective metrics. However, these metrics do not reflect the visual quality perceived by a human observer. In this work, the performances of four speckle reduction algorithms, namely, the nonlocal means-the Lee, the Frost, and the block-matching 3D filters, with varying parameterizations-were subjectively evaluated. The results were ranked with respect to the perceived image quality to obtain the mean opinion scores using pairwise comparison. The correlation between the subjective results and 20 different no-reference objective quality metrics was evaluated. The experiment indicates that block-matching 3D and Lee are the preferred filters, depending on hologram characteristics. The best-performing objective metrics were identified for each filter.

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.

Speckle denoising by variant nonlocal means methods

This study aims to demonstrate the performances of nonlocal means (NLM) and their variant denoising methods, mainly focusing on NLM-shaped adaptive patches and several NLM-reprojection schemes for speckle noise reduction in amplitude and phase images of the digital coherent imaging systems. In the digital coherent imaging systems such as digital speckle pattern interferometry, digital holographic interferometry, etc., the image quality is severely degraded by additive uncorrelated speckle noise, due to the coherent nature of the light source, and therefore limits the development of several applications of these imaging systems in many fields. NLM and its variant denoising methods are employed to denoise the intensity/phase images obtained from these imaging systems, and their effectiveness is evaluated by considering various parameters. The performance comparison of these methods with other existing speckle denoising methods is also presented. The performance of these methods for speckle noise reduction is quantified on the basis of two criteria matrices, namely, the peak-to-signal noise ratio and the image quality index. Based on these criteria matrices, it is observed that these denoising methods have the ability to improve the intensity and phase images favorably in comparison to other speckle denoising techniques, and these methods are more effective and feasible in speckle-noise reduction.

Fast single fringe-pattern processing with graphics processing unit

Optical interferometric techniques provide noncontact, full-field, and high-precision measurements that are very attractive in various research and application fields. Single fringe-pattern processing (SFPP) is often required when measuring fast phenomena, which contain multiple steps including noise removal, phase demodulation, and unwrapping. However, several difficulties are encountered during SFPP, among which the processing time is of interest due to the increasing computational load brought by the large amount and high-resolution fringe patterns in recent years. In this paper, we propose a general and complete graphics processing unit (GPU)-based SFPP framework to perform a systematic discussion on SFPP acceleration. Typical methods from the spatial domain, the transform-based, and the path-related are chosen to have a variety of methods in the framework for better parallelization demonstration, namely, coherence-enhancing diffusion for denoising, spiral phase quadrature transform for demodulation, and quality-guided phase unwrapping. To the best of our knowledge, this is the first time a complete GPU-based framework has been proposed for SFPP. The advantages of performing the analysis and parallelization in framework level are demonstrated, where processing redundancy can be identified and reduced. The proposed framework can be used as an example to demonstrate the GPU-based parallelization in SFPP. Methods in the framework can be replaced but the framework level analysis, the parallel design, and the involved functions are always good references. Experiments are performed on simulated and experimental fringe patterns to demonstrate the effectiveness of the proposed work and achieve at most 29.8 times speedup compared with CPU-based sequential processing.

Speckle noise reduction in digital speckle pattern interferometric fringes by nonlocal means and its related adaptive kernel-based methods

Digital speckle pattern interferometry (DSPI) is widely used in many scientific and industrial applications. Besides its several advantages, one of the basic problems encountered in DSPI is the undesired speckle noise existing in the fringe pattern. In this paper, we demonstrate the performance of nonlocal means (NLM) and its related adaptive kernel-based filtering methods for speckle noise reduction in DSPI fringes. The NLM filter and its related kernel-based filters such as NLM-average, NLM-local polynomial regression, and NLM-shape adaptive patches are implemented first on simulated DSPI fringes, and their performances are quantified on the basis of peak signal-to-noise ratio (PSNR), mean square error (MSE), and quality index (Q). Further, their effectiveness and abilities in reducing speckle noise are compared with other speckle denoising methods. These filtering methods are then employed on experimental DSPI fringes. The obtained results reveal that these filtering methods have the ability to improve the PSNR and Q of the DSPI fringes and provide better visual and quantitative results. It is also observed that the proposed filtering methods preserve the edge information of the DSPI fringes, which is evaluated on the basis of the edge preservation index of the resultant filtered images.

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.

Coherent noise reduction of phase images in digital holographic microscopy based on the adaptive anisotropic diffusion

The suppression of coherent noise can produce higher-quality reconstructed images in digital holographic microscopy. A robust and effective phase coherent noise denoising algorithm is proposed in this paper that combines the anisotropic diffusion equation and the phase quality map. In order to accurately identify the noise and signal pixels, we introduce the phase quality map and edge detection to quantify the quality of the pixel information. In addition, a synthetic diffusion function is established to control the speed of the anisotropic diffusion process based on the quality coefficient. Several experiments have been carried out to validate the effectiveness of the proposed algorithm for coherent noise reduction. The results demonstrate that the proposed algorithm can reduce coherent noise and preserve edge details well.

Learnable despeckling framework for optical coherence tomography images

Optical coherence tomography (OCT) is a prevalent, interferometric, high-resolution imaging method with broad biomedical applications. Nonetheless, OCT images suffer from an artifact called speckle, which degrades the image quality. Digital filters offer an opportunity for image improvement in clinical OCT devices, where hardware modification to enhance images is expensive. To reduce speckle, a wide variety of digital filters have been proposed; selecting the most appropriate filter for an OCT image/image set is a challenging decision, especially in dermatology applications of OCT where a different variety of tissues are imaged. To tackle this challenge, we propose an expandable learnable despeckling framework, we call LDF. LDF decides which speckle reduction algorithm is most effective on a given image by learning a figure of merit (FOM) as a single quantitative image assessment measure. LDF is learnable, which means when implemented on an OCT machine, each given image/image set is retrained and its performance is improved. Also, LDF is expandable, meaning that any despeckling algorithm can easily be added to it. The architecture of LDF includes two main parts: (i) an autoencoder neural network and (ii) filter classifier. The autoencoder learns the FOM based on several quality assessment measures obtained from the OCT image including signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, edge preservation index, and mean structural similarity index. Subsequently, the filter classifier identifies the most efficient filter from the following categories: (a) sliding window filters including median, mean, and symmetric nearest neighborhood, (b) adaptive statistical-based filters including Wiener, homomorphic Lee, and Kuwahara, and (c) edge preserved patch or pixel correlation-based filters including nonlocal mean, total variation, and block matching three-dimensional filtering.

Reference-free metric for quantitative noise appraisal in holographic phase measurements

This paper presents a reference-free metric for quantitative appraisal of de-noising algorithms for phase measurements in digital holography. In the literature, quality metrics are not self-contained because they require a noise-free reference phase fringe pattern in order to be computed. In practical situations, no exact phase is available to evaluate the quality of processing. In order to bypass such limitations, one needs a metric directly capable of providing information on how efficient the filtering is, without any help from any reference measurements and by only considering the measured available phase data. This paper presents a novel reference-free metric, called estimated phase error for quantitative appraisal of de-noising algorithms for noisy phase data processing. This metric is based on the computation of an estimator of the standard deviation of the phase error between data processed with an external algorithm and that from the evaluated algorithm. A benchmark, including 37 different de-noising algorithms, demonstrates that the proposed metric is capable of producing the same rankings as those obtained with classical metrics, requiring a reference phase. Application to phase data from mechanical testing demonstrates that the ranking obtained from experimental phase data is similar to that obtained during the benchmarking with simulated data.

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.

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.

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.

Quasi noise-free digital holography

One of the main drawbacks of Digital Holography (DH) is the coherent nature of the light source, which severely corrupts the quality of holographic reconstructions. Although numerous techniques to reduce noise in DH have provided good results, holographic noise suppression remains a challenging task. We propose a novel framework that combines the concepts of encoding multiple uncorrelated digital holograms, block grouping and collaborative filtering to achieve quasi noise-free DH reconstructions. The optimized joint action of these different image-denoising methods permits the removal of up to 98% of the noise while preserving the image contrast. The resulting quality of the hologram reconstructions is comparable to the quality achievable with non-coherent techniques and far beyond the current state of art in DH. Experimental validation is provided for both single-wavelength and multi-wavelength DH, and a comparison with the most used holographic denoising methods is performed.

Nonlinearity compensation and complex-to-phase conversion of complex incoherent digital holograms for optical reconstruction

Incoherent digital holography (IDH) can be realized by optical scanning holography or self-interference incoherent holography. Although IDH can exhibit high quality reconstruction due to its inherently speckle-free property, direct display of an incoherent hologram is a challenge because of its amplitude nonlinearity and the demand of complex modulation. In this paper we propose to compensate the amplitude nonlinearity at the object plane, and use bidirectional error-diffusion method to convert the complex-type incoherent Fresnel hologram to a phase-only Fresnel hologram for display. A spatial light modulator is used to reconstruct the phase-only hologram optically to demonstrate the validity of our proposed method.

Quantitative appraisal for noise reduction in digital holographic phase imaging

This paper discusses on a quantitative comparison of the performances of different advanced algorithms for phase data de-noising. In order to quantify the performances, several criteria are proposed: the gain in the signal-to-noise ratio, the Q index, the standard deviation of the phase error, and the signal to distortion ratio. The proposed methodology to investigate de-noising algorithms is based on the use of a realistic simulation of noise-corrupted phase data. A database including 25 fringe patterns divided into 5 patterns and 5 different signal-to-noise ratios was generated to evaluate the selected de-noising algorithms. A total of 34 algorithms divided into different families were evaluated. Quantitative appraisal leads to ranking within the considered criteria. A fairly good correlation between the signal-to-noise ratio gain and the quality index has been observed. There exists an anti-correlation between the phase error and the quality index which indicates that the phase errors are mainly structural distortions in the fringe pattern. Experimental results are thoroughly discussed in the paper.

Quality assessment of combined quantization-shot-noise-induced decorrelation noise in high-speed digital holographic metrology

this paper discusses on the influence of decorrelation noise induced by quantization and shot-noise when recording digital holograms at very high frame rate. A criterion based on the coherence factor of the hologram phase difference is proposed. The main parameters of interest are the ratio between the reference and the object waves and the sensor dynamics, depending on the photo-electron capacity of pixels. The study is based on a full numerical simulation of the holographic process, which provides useful rules. This leads to define the optimal conditions for recording at very-high frame rate with minimization of the decorrelation noise. Experimental results obtained with frame rate at 50kHz confirm the proposed approach.

Spatial coherence analysis for optical scanning holography

In optical scanning holography (OSH), the system can be operated in coherent mode by using a pinhole detector, or in incoherent mode by using a spatially integrating detector. In the coherent mode, the three-dimensional (3D) amplitude transparency of an object is recorded and thus the phase of the object can be retrieved. On the other hand, it is the 3D intensity transparency of the object recorded in the incoherent mode and thus the speckle can be suppressed. OSH in both coherence modes has been well investigated. However, there is no discussion on the case between the coherent mode and incoherent mode, namely, the partial-coherent mode. In this paper, we derived for the first time, to the best of our knowledge, the formula of OSH in various modes of coherence. We found the detector in OSH plays the role of a kind of filter for the field. The retrieved amplitude transparency of the object is thus nonlinearly processed by the mask function of the detector. Consequently, the reconstructed image cannot benefit from the implementation of the partial-coherent mode. On the contrary, significant artifacts usually appear among the reconstructed image and thus the image quality degrades.

Phase-shift binary digital holography

We propose phase-shift digital holography (DH) with a one-bit image sensor. In this method, the propagating complex field from an object is binarized by a one-bit sensor using a phase-shifter. The complex field on the hologram plane is then calculated with the one-bit image data. The object field is recovered via Fresnel back-propagation of the calculated hologram and filtering to suppress some artifacts caused by the binarization. The concept was demonstrated in preliminary experiments by using a synthetically binarized hologram with single-shot and multi-shot phase-shift DH.

Encoding multiple holograms for speckle-noise reduction in optical display

In digital holography (DH) a mixture of speckle and incoherent additive noise, which appears in numerical as well as in optical reconstruction, typically degrades the information of the object wavefront. Several methods have been proposed in order to suppress the noise contributions during recording or even during the reconstruction steps. Many of them are based on the incoherent combination of multiple holographic reconstructions achieving remarkable improvement, but only in the numerical reconstruction i.e. visualization on a pc monitor. So far, it has not been shown the direct synthesis of a digital hologram which provides the denoised optical reconstruction. Here, we propose a new effective method for encoding in a single complex wavefront the contribution of multiple incoherent reconstructions, thus allowing to obtain a single synthetic digital hologram that show significant speckle-reduction when optically projected by a Spatial Light Modulator (SLM).

Speckle reduction by combination of digital filter and optical suppression in a modified Gerchberg-Saxton algorithm computer-generated hologram

A speckleless illuminated modified-Gerchberg-Saxton-algorithm-type computer-generated hologram, which adopts a lower frequency of the iterative algorithm and calculation time, is proposed to code a hologram with two signals and position a multiplexing phase-only function, which can reconstruct the left and the right viewing holograms on the pupillary-distance position after the decryption and still maintain the content with high contrast and definition. The reconstructed image quality presents root mean square error of 0.03, with a diffraction efficiency of 87%, and signal-to-noise ratio of 8 dB after the analysis. Furthermore, two denoising techniques for the digital filter and optical suppression are combined, in which the speckle suppression with pseudorandom phase modulation and a rotating diffuser are utilized for successfully reducing the speckle contrast, which was reduced to below 4%. The goal was to reduce visual fatigue for the viewers.

Speckle noise suppression in digital holography by angular diversity with phase-only spatial light modulator

A speckle noise suppression method in digital holography is proposed by the angular diversity with a phase-only spatial light modulator (SLM). The minimal angular difference of illumination beams is quantitatively analyzed to ensure the noncorrelation of any two speckle patterns, and then the phase-only SLM is employed to generate a series of tilted illumination beams. Comparing with the typical methods, the tilted illumination beams are controlled dynamically and accurately, which makes it possible to record a large number of holograms. Finally, using an image-plane digital holographic system, 117 holograms are recorded respectively, and the synthesized reconstructed images are obtained with the greatly suppressed speckle noise which is in good agreement with the theoretical results. The experimental results demonstrate the effectiveness, repeatability, and practicability of the proposed approach.

Wide-field computational color imaging using pixel super-resolved on-chip microscopy

Lens-free holographic on-chip imaging is an emerging approach that offers both wide field-of-view (FOV) and high spatial resolution in a cost-effective and compact design using source shifting based pixel super-resolution. However, color imaging has remained relatively immature for lens-free on-chip imaging, since a 'rainbow' like color artifact appears in reconstructed holographic images. To provide a solution for pixel super-resolved color imaging on a chip, here we introduce and compare the performances of two computational methods based on (1) YUV color space averaging, and (2) Dijkstra's shortest path, both of which eliminate color artifacts in reconstructed images, without compromising the spatial resolution or the wide FOV of lens-free on-chip microscopes. To demonstrate the potential of this lens-free color microscope we imaged stained Papanicolaou (Pap) smears over a wide FOV of ~14 mm(2) with sub-micron spatial resolution.

Random resampling masks: a non-Bayesian one-shot strategy for noise reduction in digital holography

Holographic imaging may become severely degraded by a mixture of speckle and incoherent additive noise. Bayesian approaches reduce the incoherent noise, but prior information is needed on the noise statistics. With no prior knowledge, one-shot reduction of noise is a highly desirable goal, as the recording process is simplified and made faster. Indeed, neither multiple acquisitions nor a complex setup are needed. So far, this result has been achieved at the cost of a deterministic resolution loss. Here we propose a fast non-Bayesian denoising method that avoids this trade-off by means of a numerical synthesis of a moving diffuser. In this way, only one single hologram is required as multiple uncorrelated reconstructions are provided by random complementary resampling masks. Experiments show a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement. At the same time, we preserve the resolution of the unprocessed image.