Underwater images are subject to intricate and diverse degradation inevitably affecting the
effectiveness of underwater visual tasks. However most approaches primarily operate in the …

Understanding illumination and reducing the need for supervision pose a significant
challenge in low-light enhancement. Current approaches are highly sensitive to data usage …

Diffusion models trained on large-scale datasets have achieved remarkable progress in
image synthesis. However due to the randomness in the diffusion process they often …

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

Recent advances in low light image enhancement have been dominated by Retinex-based
learning framework, leveraging convolutional neural networks (CNNs) and Transformers …

This paper proposes a coupled learning framework to break the performance bottleneck of
infrared-visible image fusion and segmentation called MRFS. By leveraging the intrinsic …