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 326350 of 1589 papers

TitleStatusHype
PAON: A New Neuron Model using Padé Approximants0
CasSR: Activating Image Power for Real-World Image Super-Resolution0
VmambaIR: Visual State Space Model for Image RestorationCode3
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder0
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-ResolutionCode1
FeatUp: A Model-Agnostic Framework for Features at Any ResolutionCode5
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction0
Activating Wider Areas in Image Super-ResolutionCode1
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution0
Efficient Diffusion Model for Image Restoration by Residual ShiftingCode5
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution0
XPSR: Cross-modal Priors for Diffusion-based Image Super-ResolutionCode2
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning0
Text-guided Explorable Image Super-resolution0
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution0
CAMixerSR: Only Details Need More "Attention"Code3
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of ArtifactsCode2
SeD: Semantic-Aware Discriminator for Image Super-ResolutionCode2
Simple Base Frame Guided Residual Network for RAW Burst Image Super-ResolutionCode1
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-ResolutionCode2
Generative AI in Vision: A Survey on Models, Metrics and Applications0
Adaptive Convolutional Neural Network for Image Super-resolutionCode1
MambaIR: A Simple Baseline for Image Restoration with State-Space ModelCode5
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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