RHVI-FDD hierarchically decouples luminance-chrominance and then frequency components in low-light images to correct color, suppress noise, and preserve details better than prior methods.
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2026 2verdicts
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A deep learning method with an enhanced physical degradation model incorporating anisotropic light spread and hidden skyglow, trained via generative models and synthetic-real coupling, removes light pollution from night cityscape images more effectively than prior restoration techniques.
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RHVI-FDD: A Hierarchical Decoupling Framework for Low-Light Image Enhancement
RHVI-FDD hierarchically decouples luminance-chrominance and then frequency components in low-light images to correct color, suppress noise, and preserve details better than prior methods.
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Deep Light Pollution Removal in Night Cityscape Photographs
A deep learning method with an enhanced physical degradation model incorporating anisotropic light spread and hidden skyglow, trained via generative models and synthetic-real coupling, removes light pollution from night cityscape images more effectively than prior restoration techniques.