Low-light image enhancement is a notoriously challenging problem. Enhancement of low-
light images is intended to increase contrast, adjust the tone, suppress noise, and produce …

This paper presents a new solution for low-light image enhancement by collectively
exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to …

Low-light image enhancement (LLIE) investigates how to improve illumination and produce
normal-light images. The majority of existing methods improve low-light images via a global …

This paper proposes a new framework for low-light image enhancement by simultaneously
conducting the appearance as well as structure modeling. It employs the structural feature to …

Previous raw image-based low-light image enhancement methods predominantly relied on
feed-forward neural networks to learn deterministic mappings from low-light to normally …

Heterogeneous image fusion (HIF) techniques aim to enhance image quality by merging
complementary information from images captured by different sensors. Among these …

Enhancing images in low-light scenes is a challenging but widely concerned task in the
computer vision. The mainstream learning-based methods mainly acquire the enhanced …

As a critical preprocessing technique, low-illumination image enhancement has a wide
range of practical applications. It aims to improve the visual perception of a given image …

Deep neural networks have achieved remarkable progress in enhancing low-light images
by improving their brightness and eliminating noise. However, most existing methods …

This paper presents a perception-aware decomposition and fusion framework for
underwater image enhancement (UIE). Specifically, a general structural patch …