Cameras are integrated with various underwater vision systems for underwater object
detection and marine biological monitoring. However, underwater images captured by …

This paper reviews the second challenge on spectral reconstruction from RGB images, ie,
the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image …

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 …

Single image dehazing is a challenging ill-posed problem which estimates latent haze-free
images from observed hazy images. Some existing deep learning based methods are …

Image dehazing is a representative low-level vision task that estimates latent haze-free
images from hazy images. In recent years, convolutional neural network-based methods …

In recent years, Transformer networks are beginning to replace pure convolutional neural
networks (CNNs) in the field of computer vision due to their global receptive field and …

Recent progress on Transformers and multi-layer perceptron (MLP) models provide new
network architectural designs for computer vision tasks. Although these models proved to be …

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 …

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 …

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