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 226250 of 1589 papers

TitleStatusHype
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network0
UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Prompt0
Backdoor Attacks against Image-to-Image Networks0
Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKVCode2
Task-driven single-image super-resolution reconstruction of document scans0
Region Attention Transformer for Medical Image RestorationCode1
Spatially-Variant Degradation Model for Dataset-free Super-resolutionCode0
Pairwise Distance Distillation for Unsupervised Real-World Image Super-ResolutionCode1
UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution0
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution0
Deform-Mamba Network for MRI Super-Resolution0
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution0
NSD-DIL: Null-Shot Deblurring Using Deep Identity Learning0
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-ResourceCode2
ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolutionCode0
Preserving Full Degradation Details for Blind Image Super-ResolutionCode1
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction0
Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-ResolutionCode1
Burst Image Super-Resolution with Base Frame Selection0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution0
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
Improving Generative Adversarial Networks for Video Super-Resolution0
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-ResolutionCode0
Enhance the Image: Super Resolution using Artificial Intelligence in MRI0
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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
8SwinFIRPSNR29.36Unverified
9CPAT+PSNR29.36Unverified
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