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 14011425 of 1589 papers

TitleStatusHype
Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical RectificationCode0
Generative adversarial network-based image super-resolution using perceptual content lossesCode0
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual QualityCode0
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution0
Deep MR Image Super-Resolution Using Structural Priors0
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial NetworksCode0
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network0
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural NetworksCode0
Multi-scale Residual Network for Image Super-ResolutionCode0
SRFeat: Single Image Super-Resolution with Feature Discrimination0
ESRGAN: Enhanced Super-Resolution Generative Adversarial NetworksCode3
Learning Sparse Low-Precision Neural Networks With Learnable Regularization0
Wide Activation for Efficient and Accurate Image Super-ResolutionCode0
Efficient Single Image Super Resolution using Enhanced Learned Group ConvolutionsCode0
Deep Learning for Single Image Super-Resolution: A Brief ReviewCode0
Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer LearningCode0
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolutionCode1
To learn image super-resolution, use a GAN to learn how to do image degradation firstCode0
Gated Fusion Network for Joint Image Deblurring and Super-ResolutionCode0
Perceptual Video Super Resolution with Enhanced Temporal Consistency0
An Attention-Based Approach for Single Image Super Resolution0
Image Super-Resolution Using Very Deep Residual Channel Attention NetworksCode2
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis0
CT-image Super Resolution Using 3D Convolutional Neural Network0
Non-Local Recurrent Network for Image RestorationCode0
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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