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<p>Low light usually reduces image visibility, making image identification difficult. Low-light image enhancement (LLIE) aims to improve the illumination and visibility of dark images with lighting noise. LLIE has broad utility in visual applications, including object detection, image recognition, and image segmentation under low-light conditions. For real-world applications, low-light images, often with complex noise, have been subjected to joint LLIE and denoising. However, only inferior restoration performance has been achieved, attributed to two challenges: (1) in real-world dark images, noise is somewhat covered by low light, and the residual noise after denoising is inevitably amplified during enhancement; (2) the conversion of raw data to sRGB leads to information loss and introduces additional noise. Hence, existing LLIE methods trained on raw data are unsuitable for more common sRGB images. Thus, we propose a novel low-light enhancement and denoising network (RLED-Net) for real-world low-light images in the sRGB color space. In RLED-Net, a differentiable latent subspace reconstruction block (LSRB) is used to embed real-world images into low-rank subspaces to suppress noise and reduce its impact during enhancement. We then present an efficient cross-channel and shift-window transformer (CST) layer with two branches for calculating the window and channel attentions to resist degradation (e.g., speckle noise and blur) caused by noise in low-light images. Based on the CST layers, we further present a U-structure network, CSTNet, as a backbone for deep feature recovery, and construct a feature refinement block to refine the final features. Extensive experiments on both real noisy images and public image databases verify the effectiveness of the proposed RLED-Net for the simultaneous enhancement and denoising of real-world low-light images.</p>

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Seeing through the noisy dark: toward real-world low-light image enhancement and denoising

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Jiahuan Ren,
  • Zhao Zhang,
  • Richang Hong,
  • Mingliang Xu,
  • Yi Yang,
  • Shuicheng Yan

摘要

Low light usually reduces image visibility, making image identification difficult. Low-light image enhancement (LLIE) aims to improve the illumination and visibility of dark images with lighting noise. LLIE has broad utility in visual applications, including object detection, image recognition, and image segmentation under low-light conditions. For real-world applications, low-light images, often with complex noise, have been subjected to joint LLIE and denoising. However, only inferior restoration performance has been achieved, attributed to two challenges: (1) in real-world dark images, noise is somewhat covered by low light, and the residual noise after denoising is inevitably amplified during enhancement; (2) the conversion of raw data to sRGB leads to information loss and introduces additional noise. Hence, existing LLIE methods trained on raw data are unsuitable for more common sRGB images. Thus, we propose a novel low-light enhancement and denoising network (RLED-Net) for real-world low-light images in the sRGB color space. In RLED-Net, a differentiable latent subspace reconstruction block (LSRB) is used to embed real-world images into low-rank subspaces to suppress noise and reduce its impact during enhancement. We then present an efficient cross-channel and shift-window transformer (CST) layer with two branches for calculating the window and channel attentions to resist degradation (e.g., speckle noise and blur) caused by noise in low-light images. Based on the CST layers, we further present a U-structure network, CSTNet, as a backbone for deep feature recovery, and construct a feature refinement block to refine the final features. Extensive experiments on both real noisy images and public image databases verify the effectiveness of the proposed RLED-Net for the simultaneous enhancement and denoising of real-world low-light images.