Resolution and contrast enhancement in weighted subtraction microscopy by deep learning

Qiu, Yuxuan; Chen, Wei; Huang, Yuran; Xu, Yueshu; Sun, Yile; Jiang, Tao; Zhang, Zhimin; Tang, Longhua; Hao, Xiang; Kuang, Cuifang; Liu, Xu

doi:10.1016/j.optlaseng.2023.107503

Cited by 1 publication

(1 citation statement)

References 25 publications

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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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