  Low-light image enhancement (LLIE) aims at improving the illumination and
visibility of dark images with lighting noise. To handle the real-world
low-light images often with heavy and complex noise, some efforts have been
made for joint LLIE and denoising, which however only achieve inferior
restoration performance. We attribute it to two challenges: 1) in real-world
low-light images, noise is somewhat covered by low-lighting and the left noise
after denoising would be inevitably amplified during enhancement; 2) conversion
of raw data to sRGB would cause information loss and also more noise, and hence
prior LLIE methods trained on raw data are unsuitable for more common sRGB
images. In this work, we propose a novel Low-light Enhancement & Denoising
Network for real-world low-light images (RLED-Net) in the sRGB color space. In
RLED-Net, we apply a plug-and-play differentiable Latent Subspace
Reconstruction Block (LSRB) to embed the real-world images into low-rank
subspaces to suppress the noise and rectify the errors, such that the impact of
noise during enhancement can be effectively shrunk. We then present an
efficient Crossed-channel & Shift-window Transformer (CST) layer with two
branches to calculate the window and channel attentions to resist the
degradation (e.g., speckle noise and blur) caused by the noise in input images.
Based on the CST layers, we further present a U-structure network CSTNet as
backbone for deep feature recovery, and construct a feature refine block to
refine the final features. Extensive experiments on both real noisy images and
public image databases well verify the effectiveness of the proposed RLED-Net
for RLLIE and denoising simultaneously.
