Deep learning phase-unwrapping method based on adaptive noise evaluation

Xie, Xiaogao; Tian, X. H.; Shou, Zhaoyu; Zeng, Qingning; Wang, Guofu; Huang, Qingnan; Qin, Mingwei; Gao, Xi

doi:10.1364/ao.464585

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…: 6000 pairs SSIM Zhou et al 254 Wrapped phase Unwrapped phase U-Net Sim. : 158 and 1036 pairs GAN loss Xie et al 255 Wrapped phase Unwrapped phase U-Net Sim. : 17,000 pairs l 2 -norm Zhao et al 256 Wrapped phase and weighted map Unwrapped phase U-Net and ResNet Sim.…”

Section: Dl-post-processing For Phase Recoverymentioning

confidence: 99%

“…Zhou et al 254 used the GAN in the InSAR phase unwrapping and avoided the blur in the unwrapped phase by combining the l 1 loss and adversarial loss. Xie et al 255 trained four networks for different noise levels, which made each network more focused on a specific noise level. 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.…”

Section: Dl-post-processing For Phase Recoverymentioning

confidence: 99%

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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%

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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“…Refs. [7,22,23,27] consider phase unwrapping as a segmentation task and employ methods such as SegNet [28], DeepLabV3+ [24], and UNet [20] to infer the wrap count of each pixel. The ground truth of the wrap count can be expressed as…”

Section: Cnn Approachmentioning

confidence: 99%

Spatial Relation Awareness Module for Phase Unwrapping

Azuma,

Ito,

Shimobaba

2024

Photonics

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Phase unwrapping is a technique used to recover the original phase from the wrapped phase in the range (−π,π]. Various methods have been proposed for phase unwrapping. In particular, methods using convolutional neural networks (CNNs) have been extensively researched because of their high robustness against noise and fast inference speed. However, conventional CNN-based methods discard the local position information and relationships between pixels in the convolution process, resulting in poor phase-unwrapping performance. To obtain better phase unwrapping results, we propose a module that combines a global convolution network, which applies convolutional layers with a kernel size equivalent to that of the feature maps, and CoordConv, which acquires the positional relationships between pixels. We validated the performance of the proposed method by comparing it with a quality-guided path algorithm and deep learning-based phase unwrapping methods and found that the proposed method is highly robust against noise.

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“…Here, inaccurate wrap counts due to inaccurate classification, results in error of integral multiples of 2π. In another approach, one-step phase unwrapping is considered, where the DL network directly maps the input wrapped phase array to the output unwrapped phase array [23][24][25]. UNET or its variant has been used as a backbone typically in one-step phase unwrapping.…”

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 phase-unwrapping method based on adaptive noise evaluation

Cited by 10 publications

References 30 publications

On the use of deep learning for phase recovery

On the use of deep learning for phase recovery

Spatial Relation Awareness Module for Phase Unwrapping

Transformer based deep learning hybrid architecture for phase unwrapping

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