Transformer-based approaches have achieved promising performance in image restoration
tasks given their ability to model long-range dependencies which is crucial for recovering …

Camouflaged object detection (COD) is the challenging task of identifying camouflaged
objects visually blended into surroundings. Albeit achieving remarkable success, existing …

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 …

Transformer-based image restoration methods in adverse weather have achieved significant
progress. Most of them use self-attention along the channel dimension or within spatially …

Diffusion Models have shown remarkable performance in image generation tasks which are
capable of generating diverse and realistic image content. When adopting diffusion models …

Abstract All-in-one (AiO) frameworks restore various adverse weather degradations with a
single set of networks jointly. To handle various weather conditions an AiO framework is …

Abstract Most existing Low-light Image Enhancement (LLIE) methods either directly map
Low-Light (LL) to Normal-Light (NL) images or use semantic or illumination maps as guides …

Recent text-to-image (T2I) models have benefited from large-scale and high-quality data,
demonstrating impressive performance. However, these T2I models still struggle to produce …

Real-world vision tasks frequently suffer from the appearance of unexpected adverse
weather conditions, including rain, haze, snow, and raindrops. In the last decade …

Image restoration (IR) refers to the process of improving visual quality of images while
removing degradation, such as noise, blur, weather effects, and so on. Traditional IR …