Motion blur is a common photography artifact in dynamic environments that typically comes
jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on …

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-
to-image diffusion models for blind super-resolution. Specifically, by employing our time …

Recently, transformer-based methods have demonstrated impressive results in various
vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for …

Infrared small target detection (IRSTD) refers to extracting small and dim targets from blurred
backgrounds, which has a wide range of applications such as traffic management and …

Image restoration is a long-standing low-level vision problem that aims to restore high-
quality images from low-quality images (eg, downscaled, noisy and compressed images) …

Both non-local (NL) operation and sparse representation are crucial for Single Image Super-
Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non …

Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from
given low-resolution (LR) images. It is an ill-posed problem because one LR image …

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 …

We show that pre-trained Generative Adversarial Networks (GANs), eg, StyleGAN, can be
used as a latent bank to improve the restoration quality of large-factor image super …

Deep convolution-based single image super-resolution (SISR) networks embrace the
benefits of learning from large-scale external image resources for local recovery, yet most …