Quality-guided phase unwrapping implementation: an improved indexed interwoven linked list

Target-surface multiplexed quantitative dynamic phase microscopic imaging based on the transport-of-intensity equation

Microscopic phase digital imaging based on the transport of intensity equation, known as TIE, is widely used in optical measurement and biomedical imaging since it can dispense with the dependence of traditional phase imaging systems on mechanical rotational scanning and interferometry devices. In this work, we provide a single exposure target-surface multiplexed phase reconstruction (SETMPR) structure based on TIE, which is remarkably easy to construct since it directly combines a conventional bright-field inverted microscope with a special image plane transmission structure that is capable of wavefront shaping and amplification. In practice, the SETMPR is able to achieve dynamic, non-interferometric, quantitative refractive index distribution of both static optical samples and dynamic biological samples in only one shot, meaning that the only limitation of measuring frequency is the frame rate. By comparing the measurement results of a microlens array and a grating with a standard instrument, the quantitative measurement capability and accuracy are demonstrated. Subsequently, both in situ static and long-term dynamic quantitative imaging of HT22 cells were performed, while automatic image segmentation was completed by introducing machine learning methods, which verified the application prospect of this work in dynamic observation of cellular in the biomedical field.

Automated phase unwrapping in digital holography with deep learning

Digital holography can provide quantitative phase images related to the morphology and content of biological samples. After the numerical image reconstruction, the phase values are limited between -π and π; thus, discontinuity may occur due to the modulo 2π operation. We propose a new deep learning model that can automatically reconstruct unwrapped focused-phase images by combining digital holography and a Pix2Pix generative adversarial network (GAN) for image-to-image translation. Compared with numerical phase unwrapping methods, the proposed GAN model overcomes the difficulty of accurate phase unwrapping due to abrupt phase changes and can perform phase unwrapping at a twice faster rate. We show that the proposed model can generalize well to different types of cell images and has high performance compared to recent U-net models. The proposed method can be useful in observing the morphology and movement of biological cells in real-time applications.

Phase unwrapping based on adaptive image in-painting of fringe patterns in measuring gear tooth flanks by laser interferometry

Phase unwrapping in regions of abnormal fringes remains an unresolved issue. In this paper, we present an approach that combines an image-inpainting strategy based on an adaptive window which is obtained according to the density and orientation of fringe patterns and a quality-guided algorithm for phase unwrapping. First, a threshold is set to a quality map to detect the target region. Second, the target region is filled with new phase values by the adaptive image-inpainting method. Then, a quality-guided phase unwrapping algorithm is applied to this newly generated wrapped phase map. Finally, postprocessing of the unwrapped result is performed. The method is validated through several simulation and experiments. The results demonstrate that the proposed algorithm is effective in the presence of abnormal fringes.

Hierarchical quality-guided phase unwrapping algorithm

A hierarchical quality-guided phase unwrapping algorithm in a shared memory environment is proposed. First, the wrapped phase is divided into regular blocks, and local wrap counts of every block are obtained by a quality-guided strategy. Then, the gradient of the block wrap counts of adjacent blocks and the quality of every block are defined by the boundary local wrap counts of every block. Each block's data can be regarded as an abstract phase point, and the quality-guided strategy can be used again to solve the block wrap counts of each block. Finally, the absolute wrap counts of each phase point are obtained by adding local wrap counts and corresponding block wrap counts, and then the final unwrapped phase is obtained. The performance of the proposed algorithm is verified through an unwrapping experiment performed on simulated data and the real interferometric synthetic aperture sonar wrapped phase in a shared memory environment. The results show that the proposed method greatly improves phase unwrapping efficiency while maintaining the correctness of unwrapped results.

Fast quality-guided phase unwrapping algorithm through a pruning strategy: applications in dynamic interferometry

The quality-guided phase unwrapping algorithm is one of the most employed spatial algorithms due to its computational efficiency and robustness. It uses a quality map to guide the unwrapping process such that pixels are processed according to their quality values from highest to lowest. Several improvements have been proposed during the last few years with the purpose of using it in time-demanding applications. However, many of the proposals depend on the distribution of the values on the given quality map. In this paper, a novel pruning strategy based on a red-black tree data structure is proposed, whose complexity time is independent of the distribution of the given quality map. We take advantage of the partial ordering of the branches in a red-black tree together with a pruning strategy to speed up the unwrapping process. Experimental results, using real and simulated data, show that the complexity time of our proposal improves the existing quality-guide-based algorithms. Also, a series of interferometric patterns of a time-varying phase distribution experiment have been processed showing that our proposal can be used for real-time applications. The source code of the implemented algorithms is publicly available.

Weighted least-squares phase unwrapping algorithm based on a non-interfering image of an object

Conventional quality maps for weighted least-squares phase unwrapping are not appropriate in regions of abnormal fringes. In this paper, the use of a non-interfering image of an object for a reliability map is proposed for robust phase unwrapping. First, the conventional weighted least-squares algorithm is applied, and the unreliable region is detected. Then, the unreliable region is unwrapped iteratively using the non-interfering image of the object. Finally, both phase maps are combined and smoothed by a continuity operation. Experimental results show that the proposed algorithm is appropriate for unwrapping phase maps of abnormal fringes.

Efficient multiscale phase unwrapping methodology with modulo wavelet transform

Many robust phase unwrapping algorithms are computationally very time-consuming, making them impractical for handling large datasets or real-time applications. In this paper, we propose a generic framework using a novel wavelet transform that can be combined with many types of phase unwrapping algorithms. By inserting reversible modulo operators in the wavelet transform, the number of coefficients that need to be unwrapped is significantly reduced, which results in large computational gains. The algorithm is tested on various types of wrapped phase imagery, reporting speedup factors of up to 500. The source code of the algorithm is publicly available.

Snake-assisted quality-guided phase unwrapping for discontinuous phase fields

Quality-guided phase unwrapping (QGPU) has been proven as an effective phase unwrapping method. As the QGPU assumes that the true phase map is continuous, it suffers from the discontinuity problem. To solve this problem, the windowed Fourier transform (WFT) based methods have been adopted to obtain effective quality measures. Nevertheless, in some special cases, using the quality maps from the WFT-based methods could not lead to a correct result. In this paper, we propose a method that incorporates the GVF snake model to assist the unwrapping process. This new method achieves better results than using the WFT-based methods directly in some discontinuous examples.