Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

This paper reviews the first challenge on single image super-resolution (restoration of rich
details in an low resolution image) with focus on proposed solutions and results. A new …

Though many attempts have been made in blind super-resolution to restore low-resolution
images with unknown and complex degradations, they are still far from addressing general …

We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3
adapts denoising diffusion probabilistic models (Ho et al. 2020),(Sohl-Dickstein et al. 2015) …

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 …

Convolutional neural network (CNN) depth is of crucial importance for image super-
resolution (SR). However, we observe that deeper networks for image SR are more difficult …

Abstract The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work
that is capable of generating realistic textures during single image super-resolution …

While it is nearly effortless for humans to quickly assess the perceptual similarity between
two images, the underlying processes are thought to be quite complex. Despite this, the …

Recent studies have shown the importance of modeling long-range interactions in the
inpainting problem. To achieve this goal, existing approaches exploit either standalone …

Deep convolutional networks have become a popular tool for image generation and
restoration. Generally, their excellent performance is imputed to their ability to learn realistic …