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 51–75 of 1589 papers

TitleStatusHype
WaveMixSR-V2: Enhancing Super-resolution with Higher EfficiencyCode2
SSL: A Self-similarity Loss for Improving Generative Image Super-resolutionCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
RealViformer: Investigating Attention for Real-World Video Super-ResolutionCode2
Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKVCode2
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-ResourceCode2
Binarized Diffusion Model for Image Super-ResolutionCode2
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image GenerationCode2
IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation ModelCode2
CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Frequency-Assisted Mamba for Remote Sensing Image Super-ResolutionCode2
HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-ResolutionCode2
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image RestorationCode2
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-ResolutionCode2
SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolutionCode2
Partial Large Kernel CNNs for Efficient Super-ResolutionCode2
Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-ResolutionCode2
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-ResolutionCode2
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual LossCode2
CFAT: Unleashing TriangularWindows for Image Super-resolutionCode2
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
XPSR: Cross-modal Priors for Diffusion-based Image Super-ResolutionCode2
SeD: Semantic-Aware Discriminator for Image Super-ResolutionCode2
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of ArtifactsCode2
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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
6DRCTPSNR28.06—Unverified
7CPAT+PSNR28.06—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