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 …

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

Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …

Global operations, such as global average pooling, are widely used in top-performance
image restorers. They aggregate global information from input features along entire spatial …

Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur.
While deep learning approach shows great promise in solving image restoration problems …

In this paper we introduce a new perspective for improving image restoration by removing
degradation in the textual representations of a given degraded image. Intuitively restoration …

Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a
defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus …

Latest diffusion-based methods for many image restoration tasks outperform traditional
models, but they encounter the long-time inference problem. To tackle it, this paper …

Lightfield phase modulation has become an effective implementation for extending depth-of-
field (DOF) of computational imaging. However, correct reconstruction of tiny details and …

Text-driven diffusion models have become increasingly popular for various image editing
tasks, including inpainting, stylization, and object replacement. However, it still remains an …