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
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
Edge-enhanced Feature Distillation Network for Efficient Super-ResolutionCode1
Self-Calibrated Efficient Transformer for Lightweight Super-ResolutionCode1
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-ResolutionCode1
Fast and Memory-Efficient Network Towards Efficient Image Super-ResolutionCode1
Cylin-Painting: Seamless 360 Panoramic Image Outpainting and BeyondCode1
Dual-Stage Approach Toward Hyperspectral Image Super-ResolutionCode1
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-ResolutionCode1
Bayesian Image Super-Resolution with Deep Modeling of Image StatisticsCode1
Efficient and Degradation-Adaptive Network for Real-World Image Super-ResolutionCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
ARM: Any-Time Super-Resolution MethodCode1
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolutionCode1
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-ResolutionCode1
Unfolded Deep Kernel Estimation for Blind Image Super-resolutionCode1
Learning the Degradation Distribution for Blind Image Super-ResolutionCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-ResolutionCode1
Gradient Variance Loss for Structure-Enhanced Image Super-ResolutionCode1
Revisiting RCAN: Improved Training for Image Super-ResolutionCode1
Hyperspectral Image Super-resolution with Deep Priors and Degradation Model InversionCode1
Flexible Style Image Super-Resolution using Conditional ObjectiveCode1
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
Detail-Preserving Transformer for Light Field Image Super-ResolutionCode1
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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