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 51–100 of 1589 papers

TitleStatusHype
Real-World Super-Resolution via Kernel Estimation and Noise InjectionCode2
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of ArtifactsCode2
Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image RestorationCode2
CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
See More Details: Efficient Image Super-Resolution by Experts MiningCode2
Spatially-Adaptive Feature Modulation for Efficient Image Super-ResolutionCode2
Neural Fields with Thermal Activations for Arbitrary-Scale Super-ResolutionCode2
Learning Generative Structure Prior for Blind Text Image Super-resolutionCode2
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-ResolutionCode2
MaIR: A Locality- and Continuity-Preserving Mamba for Image RestorationCode2
SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolutionCode2
NTIRE 2025 Challenge on Image Super-Resolution (4): Methods and ResultsCode2
Image Restoration with Mean-Reverting Stochastic Differential EquationsCode2
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
Image Super-Resolution using Efficient Striped Window TransformerCode2
Binarized Diffusion Model for Image Super-ResolutionCode2
CFAT: Unleashing TriangularWindows for Image Super-resolutionCode2
Frequency-Assisted Mamba for Remote Sensing Image Super-ResolutionCode2
Image Super-Resolution Using Very Deep Residual Channel Attention NetworksCode2
Omni Aggregation Networks for Lightweight Image Super-ResolutionCode2
Emulating Self-attention with Convolution for Efficient Image Super-ResolutionCode2
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-ResourceCode2
Efficient Long-Range Attention Network for Image Super-resolutionCode2
Effective Diffusion Transformer Architecture for Image Super-ResolutionCode2
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
AIM 2020 Challenge on Efficient Super-Resolution: Methods and ResultsCode2
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution NetworkCode2
Efficient and Explicit Modelling of Image Hierarchies for Image RestorationCode2
Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-ResolutionCode2
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution PriorsCode2
Dual Aggregation Transformer for Image Super-ResolutionCode2
Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-ResolutionCode2
HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-ResolutionCode2
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
CFAT: Unleashing Triangular Windows for Image Super-resolutionCode2
Implicit Diffusion Models for Continuous Super-ResolutionCode2
IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation ModelCode2
Learning Continuous Image Representation with Local Implicit Image FunctionCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
Auto-Encoded Supervision for Perceptual Image Super-ResolutionCode2
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-ResolutionCode2
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual LearningCode2
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-ResolutionCode2
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual LossCode2
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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
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—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