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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1175 of 1589 papers

TitleStatusHype
Convolutional Sparse Coding for Image Super-Resolution—0
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution—0
Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution—0
Convolutional Low-Resolution Fine-Grained Classification—0
QMambaBSR: Burst Image Super-Resolution with Query State Space Model—0
Controlling Neural Networks via Energy Dissipation—0
Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views—0
Dynamic Attention-Guided Diffusion for Image Super-Resolution—0
Quantum Annealing for Single Image Super-Resolution—0
Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution—0
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution—0
Raising The Limit Of Image Rescaling Using Auxiliary Encoding—0
RAISR: Rapid and Accurate Image Super Resolution—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
Contrastive Learning for Climate Model Bias Correction and Super-Resolution—0
UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution—0
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank—0
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision—0
A Comprehensive Survey of Transformers for Computer Vision—0
RDRN: Recursively Defined Residual Network for Image Super-Resolution—0
Real Image Super-Resolution using GAN through modeling of LR and HR process—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—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