Spectral super-resolution (sSR) is a very important technique to obtain hyperspectral images
from only RGB images, which can effectively overcome the high acquisition cost and low …

Abstract Single Image D eraining (SID) aims at recovering the rain-free background from an
image degraded by rain streaks. For the powerful fitting ability of deep neural networks and …

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 …

In this paper, we study a challenging problem in image restoration, namely, how to develop
an all-in-one method that could recover images from a variety of unknown corruption types …

In this paper, we present Uformer, an effective and efficient Transformer-based architecture
for image restoration, in which we build a hierarchical encoder-decoder network using the …

Removing adverse weather conditions like rain, fog, and snow from images is an important
problem in many applications. Most methods proposed in the literature have been designed …

As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …

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

In this paper, an ill-posed problem of multiple adverse weather removal is investigated. Our
goal is to train a model with a'unified'architecture and only one set of pretrained weights that …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …