In the era of information, characterized by an exponential growth in data volume and an
escalating level of data abstraction, there has been a substantial focus on brain‐like chips …

Transformers-based methods have achieved significant performance in image deraining as
they can model the non-local information which is vital for high-quality image reconstruction …

Recent years have witnessed significant advances in image deraining due to the kinds of
effective image priors and deep learning models. As each deraining approach has …

Image restoration under multiple adverse weather conditions aims to remove weather-
related artifacts by using the single set of network parameters. In this paper, we find that …

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

In real-world scenarios, image impairments often manifest as composite degradations,
presenting a complex interplay of elements such as low light, haze, rain, and snow. Despite …

Single image deraining aims to remove rain perturbation while restoring the clean
background scene from a rain image. However, existing methods tend to produce blurry and …

Restoring high-quality images from degraded hazy observations is a fundamental and
essential task in the field of computer vision. While deep models have achieved significant …

Image segmentation based on continual learning exhibits a critical drop of performance,
mainly due to catastrophic forgetting and background shift, as they are required to …

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 …