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) …

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 …

This paper introduces a novel large dataset for example-based single image super-
resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The …

Abstract Recently, Convolutional Neural Network (CNN) based models have achieved great
success in Single Image Super-Resolution (SISR). Owing to the strength of deep networks …

Single image super-resolution is the task of inferring a high-resolution image from a single
low-resolution input. Traditionally, the performance of algorithms for this task is measured …

Recent advances in video super-resolution have shown that convolutional neural networks
combined with motion compensation are able to merge information from multiple low …

Super-Resolution convolutional neural networks have recently demonstrated high-quality
restoration for single images. However, existing algorithms often require very deep …

Video super-resolution (VSR) aims to utilize multiple low-resolution frames to generate a
high-resolution prediction for each frame. In this process, inter-and intra-frames are the key …

Normalizing Flows (NFs) are able to model complicated distributions p (y) with strong inter-
dimensional correlations and high multimodality by transforming a simple base density p (z) …

Generative adversarial networks (GANs) have recently been adopted to single image super
resolution (SISR) and showed impressive results with realistically synthesized high …