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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 926–950 of 1589 papers

TitleStatusHype
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—0
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning—0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN—0
A deep primal-dual proximal network for image restoration—0
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment—0
Local Patch Encoding-Based Method for Single Image Super-Resolution—0
Local-Selective Feature Distillation for Single Image Super-Resolution—0
LocalSR: Image Super-Resolution in Local Region—0
Local Statistics for Generative Image Detection—0
Dual-domain Modulation Network for Lightweight Image Super-Resolution—0
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models—0
AdderSR: Towards Energy Efficient Image Super-Resolution—0
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES—0
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
Training Set Effect on Super Resolution for Automated Target Recognition—0
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks—0
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks—0
MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge Distillation—0
Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning—0
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping—0
DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors—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