Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …

Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input
images, which is a domain-specific image restoration problem in the low-level computer …

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 …

Image restoration under adverse weather conditions has been of significant interest for
various computer vision applications. Recent successful methods rely on the current …

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 …

Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …

Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart.
Besides dealing with this long-standing task in the spatial domain, a few approaches seek …

Learning single image deraining (SID) networks from an unpaired set of clean and rainy
images is practical and valuable as acquiring paired real-world data is almost infeasible …

Existing rain-removal algorithms often tackle either rain streak removal or raindrop removal,
and thus may fail to handle real-world rainy scenes. Besides, the lack of real-world deraining …

Rainy weather is a challenge for many vision-oriented tasks (eg, object detection and
segmentation), which causes performance degradation. Image deraining is an effective …