NTIRE 2023 challenge on stereo image super-resolution: Methods and results
In this paper, we summarize the 2nd NTIRE challenge on stereo image super-resolution
(SR) with a focus on new solutions and results. The task of the challenge is to super-resolve …
(SR) with a focus on new solutions and results. The task of the challenge is to super-resolve …
AIM 2024 challenge on video super-resolution quality assessment: Methods and results
This paper presents the Video Super-Resolution (SR) Quality Assessment (QA) Challenge
that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction …
that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction …
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts
Super-resolution (SR) is an ill-posed inverse problem where the size of the set of feasible
solutions that are consistent with a given low-resolution image is very large. Many …
solutions that are consistent with a given low-resolution image is very large. Many …
SkipDiff: Adaptive Skip Diffusion Model for High-Fidelity Perceptual Image Super-resolution
It is well-known that image quality assessment usually meets with the problem of perception-
distortion (pd) tradeoff. The existing deep image super-resolution (SR) methods either focus …
distortion (pd) tradeoff. The existing deep image super-resolution (SR) methods either focus …
Perception-distortion balanced super-resolution: A multi-objective optimization perspective
High perceptual quality and low distortion degree are two important goals in image
restoration tasks such as super-resolution (SR). Most of the existing SR methods aim to …
restoration tasks such as super-resolution (SR). Most of the existing SR methods aim to …
Boosting Flow-based Generative Super-Resolution Models via Learned Prior
Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in
generating high-quality images. However these methods encounter several challenges …
generating high-quality images. However these methods encounter several challenges …
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World
H Fu, F Peng, X Li, Y Li, X Wang… - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
Most current arbitrary-scale image super-resolution (SR) methods has commonly relied on
simulated data generated by simple synthetic degradation models (eg bicubic …
simulated data generated by simple synthetic degradation models (eg bicubic …
Analysis of medical images super-resolution via a wavelet pyramid recursive neural network constrained by wavelet energy entropy
Recently, multi-resolution pyramid-based techniques have emerged as the prevailing
research approach for image super-resolution. However, these methods typically rely on a …
research approach for image super-resolution. However, these methods typically rely on a …
Uncertainty-Aware GAN for Single Image Super Resolution
C Ma - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
Generative adversarial network (GAN) has become a popular tool in the perceptual-oriented
single image super-resolution (SISR) for its excellent capability to hallucinate details …
single image super-resolution (SISR) for its excellent capability to hallucinate details …
Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem
In this paper, we introduce a novel approach to single-image super-resolution (SISR) that
balances perceptual quality and distortion through multi-objective optimization (MOO) …
balances perceptual quality and distortion through multi-objective optimization (MOO) …