Leveraging spatial coherence as a physical prior to guide the training of a deep neural network, TWC-Swin method excels at capturing both local and global image features and eliminates image degradation caused by arbitrary turbulence. (IMAGE)

SPIE--International Society for Optics and Photonics

Leveraging spatial coherence as a physical prior to guide the training of a deep neural network, TWC-Swin method excels at capturing both local and global image features and eliminates image degradation caused by arbitrary turbulence.

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Leveraging spatial coherence as a physical prior to guide the training of a deep neural network, TWC-Swin method excels at capturing both local and global image features and eliminates image degradation caused by arbitrary turbulence. 

Credit

X. Tong et al., doi 10.1117/1.AP.5.6.066003.

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