This work reviews the results of the NTIRE 2021 Challenge on Non-Homogeneous
Dehazing. The proposed techniques and their results have been evaluated on a novel …

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part
of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in …

Although there have been significant advances in the field of image restoration recently, the
system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may …

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 …

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 …

Learning to leverage the relationship among diverse image restoration tasks is quite
beneficial for unraveling the intrinsic ingredients behind the degradation. Recent years have …

We introduce a set of image transformations that can be used as corruptions to evaluate the
robustness of models as well as data augmentation mechanisms for training neural …

Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image
and the blur kernel given a blurry image. Recent progress on image deblurring always …

How to aggregate spatial information plays an essential role in learning-based image
restoration. Most existing CNN-based networks adopt static convolutional kernels to encode …