In the last few years, there have been several revolutions in the field of deep learning,
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …

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 …

Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis
process into a sequential application of a denoising network. However, different from image …

Image restoration involves recovering a high-quality clean image from its degraded version.
Deep learning-based methods have significantly improved image restoration performance …

The aim of this paper is to propose a mechanism to efficiently and explicitly model image
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …

As the quality of optical sensors improves, there is a need for processing large-scale
images. In particular, the ability of devices to capture ultra-high definition (UHD) images and …

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 …

This paper presents a stochastic differential equation (SDE) approach for general-purpose
image restoration. The key construction consists in a mean-reverting SDE that transforms a …

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 …