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 1401–1425 of 1589 papers

TitleStatusHype
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN—0
Triple Attention Mixed Link Network for Single Image Super Resolution—0
Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control—0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report—0
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation NetworksCode0
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution—0
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation—0
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution—0
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields—0
Kernel based low-rank sparse model for single image super-resolution—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—0
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-Resolution—0
Deep MR Image Super-Resolution Using Structural Priors—0
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial NetworksCode0
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network—0
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural NetworksCode0
SRFeat: Single Image Super-Resolution with Feature Discrimination—0
Multi-scale Residual Network for Image Super-ResolutionCode0
Learning Sparse Low-Precision Neural Networks With Learnable Regularization—0
Wide Activation for Efficient and Accurate Image Super-ResolutionCode0
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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