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Better enhancement of dim noisy images by using subspaces with RLED-Net

Better enhancement of dim noisy images by using subspaces with RLED-Net


Seeing Through The Noisy Dark: Toward Real-world Low-Light Image Enhancement and Denoising

arXiv paper abstract https://arxiv.org/abs/2210.00545



Images collected in real-world low-light environment usually suffer from lower visibility and heavier noise, due to the insufficient light or hardware limitation.


... existing low-light image enhancement (LLIE) methods basically ignored the noise interference and mainly focus on refining the illumination of the low-light images based on benchmarked noise-negligible datasets.


... consider the task of seeing through the noisy dark in sRGB color space, and propose a novel end-to-end method termed Real-world Low-light Enhancement & Denoising Network (RLED-Net).


Since natural images can usually be characterized by low-rank subspaces in which the redundant information and noise can be removed, ... design a Latent Subspace Reconstruction Block (LSRB) for feature extraction and denoising.


To reduce the loss of global feature (e.g., color/shape information) and extract more accurate local features (e.g., edge/texture information), ... also present a basic layer with two branches, called Cross-channel & Shift-window Transformer (CST).


... verified the effectiveness of ... RLED-Net for both RLLIE and denoising.



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