Low-light photography presents significant challenges. Multi-image processing methods
have made numerous attempts to obtain high-quality photos yet remain unsatisfactory …

Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

We present DiffBIR, a general restoration pipeline that could handle different blind image
restoration tasks in a unified framework. DiffBIR decouples blind image restoration problem …

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 …

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

In this paper, we explore the role of Instance Normalization in low-level vision tasks.
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to …

Image restoration tasks demand a complex balance between spatial details and high-level
contextualized information while recovering images. In this paper, we propose a novel …

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 …