Deep learning based single shot multiple phase derivative retrieval method in multi-wave digital holographic interferometry

Vithin, Allaparthi Venkata Satya; Ramaiah, Jagadesh; Gannavarpu, Rajshekhar

doi:10.1016/j.optlaseng.2022.107442

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…Zhao et al 256 added a weighted map as the prior to the neural network to make it more focused on the area near the jump edge, similar to an additional attention mechanism. Different from the above methods, Vithin et al 276 , 277 proposed to use the Y-Net 90 to infer the phase gradients from a wrapped phase and then calculate the absolute phase.…”

Section: Dl-post-processing For Phase Recoverymentioning

confidence: 99%

On the use of deep learning for phase recovery

Wang,

Song,

Wang

et al. 2024

Light Sci Appl

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Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.

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Section: Dl-post-processing For Phase Recoverymentioning

confidence: 99%

On the use of deep learning for phase recovery

Wang,

Song,

Wang

et al. 2024

Light Sci Appl

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“…Spatial unwrapping techniques such as minimum norm, quality guided methods etc have limitations in handling the phase discontinuities and disjoint regions whereas temporal techniques requires multi frames or multi frequency fringes [5]. Deep learning is gaining attraction in various fields such as microscopy, holography, super resolution imaging, optical image encryption, interferometry, natural language processing (NLP), facial recognition, autonomous vehicles, medical image analysis, drug discovery, disease diagnosis, treatment recommendation, etc [7][8][9][10][11]. Advent of newer and complex architectures because of the availability of necessary hardware such as Graphics Processing Units (GPUs) and Tensor Processing units (TPUs) has improved the performance of deep learning in various fields [12].…”

Section: Introductionmentioning

confidence: 99%

Transformer based deep learning hybrid architecture for phase unwrapping

Bujagouni,

Pradhan

2024

Phys. Scr.

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A deep learning Hybrid architecture for phase unwrapping has been proposed. The hybrid architecture is based on integration of Convolutional Neural Networks (CNN) with Vision Transformer. The performance of Hybrid architecture/network in phase unwrapping is compared against CNN based standard UNET network. Structural Similarity Index (SSIM) and Root Mean Square Error (RMSE) have been used as performance metrics to assess the performance of these deep learning networks for phase unwrapping. To train and test the networks, dataset with high mean Entropy has been generated using Gaussian filtering of random noise in Fourier plane. The Hybrid architecture is tested on test dataset and is found to have superior performance metrics against the UNET network. Their performance is also tested in noisy environment with various noise levels and Hybrid architecture demonstrated better anti-noise capability than UNET network. Hybrid architecture was successfully validated in real world scenario using experimental data from custom built Digital Holographic Microscope. With the advent of newer architectures and hardware, Deep learning networks can further improve the performance in solving inverse problems.

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Deep learning assisted non-contact defect identification method using diffraction phase microscopy

Narayan,

Vithin,

Gannavarpu

2023

Appl. Opt.

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Reliable detection of defects from optical fringe patterns is a crucial problem in non-destructive optical interferometric metrology. In this work, we propose a deep-learning-based method for fringe pattern defect identification. By attributing the defect information to the fringe pattern’s phase gradient, we compute the spatial phase derivatives using the deep learning model and apply the gradient map to localize the defect. The robustness of the proposed method is illustrated on multiple numerically synthesized fringe pattern defects at various noise levels. Further, the practical utility of the proposed method is substantiated for experimental defect identification in diffraction phase microscopy.

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Deep learning based single shot multiple phase derivative retrieval method in multi-wave digital holographic interferometry

Cited by 7 publications

References 39 publications

On the use of deep learning for phase recovery

On the use of deep learning for phase recovery

Transformer based deep learning hybrid architecture for phase unwrapping

Deep learning assisted non-contact defect identification method using diffraction phase microscopy

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