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 626650 of 1589 papers

TitleStatusHype
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution0
U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image FusionCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information0
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
Learning Detail-Structure Alternative Optimization for Blind Super-ResolutionCode1
Global Learnable Attention for Single Image Super-ResolutionCode1
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space ModelCode4
MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution0
From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-ResolutionCode1
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution0
Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory0
Knowledge Distillation based Degradation Estimation for Blind Super-ResolutionCode1
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution0
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture0
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
GAN Prior based Null-Space Learning for Consistent Super-ResolutionCode1
Perception-Oriented Single Image Super-Resolution using Optimal Objective EstimationCode1
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution0
N-Gram in Swin Transformers for Efficient Lightweight Image Super-ResolutionCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network0
RDRN: Recursively Defined Residual Network for Image Super-Resolution0
Super-resolution Reconstruction of Single Image for Latent features0
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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