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 401425 of 1589 papers

TitleStatusHype
Diffusion-based Blind Text Image Super-ResolutionCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Attention in Attention Network for Image Super-ResolutionCode1
Face Super-Resolution Using Stochastic Differential EquationsCode1
Fast Nearest Convolution for Real-Time Efficient Image Super-ResolutionCode1
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-ResolutionCode1
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-ResolutionCode1
Exploring Separable Attention for Multi-Contrast MR Image Super-ResolutionCode1
Neural Sparse Representation for Image RestorationCode1
NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator DiffusionCode1
Exploring the Low-Pass Filtering Behavior in Image Super-ResolutionCode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
AdaDM: Enabling Normalization for Image Super-ResolutionCode1
Creating High Resolution Images with a Latent Adversarial GeneratorCode1
DREAM: Diffusion Rectification and Estimation-Adaptive ModelsCode1
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile DevicesCode1
C3-STISR: Scene Text Image Super-resolution with Triple CluesCode1
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and ResultsCode1
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
Does Diffusion Beat GAN in Image Super Resolution?Code1
Frequency Domain-based Perceptual Loss for Super ResolutionCode1
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
One Step Diffusion-based Super-Resolution with Time-Aware DistillationCode1
ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolutionCode1
Attaining Real-Time Super-Resolution for Microscopic Images Using GANCode1
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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