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

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Showing 801–825 of 1589 papers

TitleStatusHype
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution—0
FAN: Frequency Aggregation Network for Real Image Super-resolution—0
Fast and Accurate Image Upscaling With Super-Resolution Forests—0
Fast Image Super-Resolution Based on In-Place Example Regression—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Fast Single Image Super-Resolution—0
Fast single image super-resolution based on sigmoid transformation—0
Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution—0
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution—0
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces—0
Feature Super-Resolution: Make Machine See More Clearly—0
Federated Learning for Blind Image Super-Resolution—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
Fine-Grained Neural Architecture Search—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Fingerprints of Super Resolution Networks—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

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