In this paper, we rethink the low-light image enhancement task and propose a physically
explainable and generative diffusion model for low-light image enhancement, termed as Diff …

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

In this paper, we propose a diffusion-based unsupervised framework that incorporates
physically explainable Retinex theory with diffusion models for low-light image …

Infrared and visible image fusion targets to provide an informative image by combining
complementary information from different sensors. Existing learning-based fusion …

Enhancing underwater images captured under mixed artificial and natural lighting
conditions presents two critical challenges. Existing methods lack a unified luminance …

Underwater imaging systems have evolved into essential hardware equipment for
develo** and utilizing marine resources. However, the complex underwater physical …

The correction of exposure-related issues is a pivotal component in enhancing the quality of
images, offering substantial implications for various computer vision tasks. Historically, most …

Low-light remote sensing (RS) images generally feature high resolution and high spatial
complexity, with continuously distributed surface features in space. This continuity in scenes …

High-quality underwater imaging is crucial for underwater exploration. However, particle
scattering and light absorption by seawater significantly degrade image clarity. To address …

Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially
for enhancing visual quality and/or extracting aggregated features for perception. However …