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 601–650 of 1589 papers

TitleStatusHype
Deep Residual Axial Networks—0
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening FrameworkCode0
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution—0
Lightweight Image Super-Resolution with Superpixel Token InteractionCode1
Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-ResolutionCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
MSRA-SR: Image Super-resolution Transformer with Multi-scale Shared Representation Acquisition—0
Content-Aware Local GAN for Photo-Realistic Super-Resolution—0
Deep Random Projector: Accelerated Deep Image PriorCode1
Spectral Bayesian Uncertainty for Image Super-Resolution—0
Toward Accurate Post-Training Quantization for Image Super ResolutionCode0
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis—0
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective—0
Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-ResolutionCode1
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid NetworkCode0
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels—0
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images—0
Infrared Image Super-Resolution: Systematic Review, and Future TrendsCode1
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach—0
Reference-based Image and Video Super-Resolution via C2-MatchingCode2
Meta-Learned Kernel For Blind Super-Resolution Kernel EstimationCode1
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution—0
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 Information—0
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-resolution—0
From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-ResolutionCode1
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution—0
Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory—0
Knowledge Distillation based Degradation Estimation for Blind Super-ResolutionCode1
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution—0
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture—0
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-Resolution—0
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 Network—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
RDRN: Recursively Defined Residual Network for Image Super-Resolution—0
Super-resolution Reconstruction of Single Image for Latent features—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