SOTAVerified

Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

Papers

Showing 726750 of 1589 papers

TitleStatusHype
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment0
Large coordinate kernel attention network for lightweight image super-resolution0
NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-ResolutionCode0
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-ResolutionCode0
An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution0
Single Image Super-Resolution Based on Global-Local Information Synergy0
Detail-Enhancing Framework for Reference-Based Image Super-Resolution0
Federated Learning for Blind Image Super-Resolution0
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimationCode0
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey0
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation0
Single-sample image-fusion upsampling of fluorescence lifetime images0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model0
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution0
Differentiable Search for Finding Optimal Quantization Strategy0
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures0
Efficient Learnable Collaborative Attention for Single Image Super-Resolution0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks0
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network0
A Study in Dataset Pruning for Image Super-Resolution0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution0
PAON: A New Neuron Model using Padé Approximants0
CasSR: Activating Image Power for Real-World Image Super-Resolution0
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
10CPATPSNR29.34Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16Unverified
2HMA†PSNR28.13Unverified
3Hi-IR-LPSNR28.13Unverified
4HAT-LPSNR28.09Unverified
5HAT_FIRPSNR28.07Unverified
6CPAT+PSNR28.06Unverified
7DRCTPSNR28.06Unverified
8HATPSNR28.05Unverified
9CPATPSNR28.04Unverified
10SwinFIRPSNR28.03Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72Unverified
2DRCT-LPSNR28.7Unverified
3HMA†PSNR28.69Unverified
4HAT-LPSNR28.6Unverified
5HAT_FIRPSNR28.43Unverified
6DRCTPSNR28.4Unverified
7HATPSNR28.37Unverified
8CPAT+PSNR28.33Unverified
9CPATPSNR28.22Unverified
10PFTPSNR28.2Unverified