Image restoration (IR) has been an indispensable and challenging task in the low-level
vision field, which strives to improve the subjective quality of images distorted by various …

We introduce a novel sequential modeling approach which enables learning a Large Vision
Model (LVM) without making use of any linguistic data. To do this we define a common …

In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various
tasks with only a handful of prompts and examples. But in computer vision, the difficulties for …

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 …

Rain can cause performance degradation of outdoor computer vision tasks. Thus, the
exploration of rain removal from videos or a single image has drawn considerable attention …

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 …

Since convolutional neural networks (CNNs) perform well at learning generalizable image
priors from large-scale data, these models have been extensively applied to image …

Deep neural networks (DNN) have achieved great success in image restoration. However,
most DNN methods are designed as a black box, lacking transparency and interpretability …

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 …

Global modeling-based image restoration frameworks have become popular. However, they
often require a high memory footprint and do not consider task-specific degradation. Our …