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 15511589 of 1589 papers

TitleStatusHype
Conditioned Regression Models for Non-Blind Single Image Super-Resolution0
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding0
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution0
Fidelity-Naturalness Evaluation of Single Image Super Resolution0
Super-Resolution with Deep Convolutional Sufficient StatisticsCode0
Accurate Image Super-Resolution Using Very Deep Convolutional NetworksCode0
Deeply-Recursive Convolutional Network for Image Super-ResolutionCode0
Seven ways to improve example-based single image super resolution0
Fast Single Image Super-Resolution0
Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples0
Is Image Super-resolution Helpful for Other Vision Tasks?0
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration0
Deep Networks for Image Super-Resolution with Sparse Prior0
Boosting Optical Character Recognition: A Super-Resolution Approach0
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution0
Single Image Super-Resolution From Transformed Self-Exemplars0
Metric Imitation by Manifold Transfer for Efficient Vision Applications0
Bayesian Sparse Representation for Hyperspectral Image Super Resolution0
Fast and Accurate Image Upscaling With Super-Resolution Forests0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction0
Self-Tuned Deep Super Resolution0
Single image super-resolution by approximated Heaviside functions0
Learning Super-Resolution Jointly from External and Internal Examples0
Image Super-Resolution Using Deep Convolutional NetworksCode1
Higher-order MRFs based image super resolution: why not MAP?0
Single Image Super Resolution via Manifold Approximation0
High Resolution 3D Shape Texture from Multiple Videos0
Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution0
Single Image Super-resolution using Deformable Patches0
A Reverse Hierarchy Model for Predicting Eye Fixations0
Structure Tensor Based Image Interpolation Method0
Sparse Coding Approach for Multi-Frame Image Super Resolution0
Single image super resolution in spatial and wavelet domain0
Simultaneous Super-Resolution of Depth and Images Using a Single Camera0
Fast Image Super-Resolution Based on In-Place Example Regression0
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution0
Depth Super Resolution by Rigid Body Self-Similarity in 3D0
A Convex Approach for Image Hallucination0
Analysis Operator Learning and Its Application to Image Reconstruction0
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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