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
arXiv PDF paper https://arxiv.org/ftp/arxiv/papers/2210/2210.00545.pdf
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.
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
LinkedIn https://www.linkedin.com/in/morris-lee-47877b7b
