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 1301–1350 of 1589 papers

TitleStatusHype
Progressive Perception-Oriented Network for Single Image Super-ResolutionCode0
Image Super-Resolution Using a Wavelet-based Generative Adversarial NetworkCode0
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal imagesCode0
Hybrid Residual Attention Network for Single Image Super ResolutionCode0
Enhanced generative adversarial network for 3D brain MRI super-resolution—0
Gated Multiple Feedback Network for Image Super-ResolutionCode0
FC^2N: Fully Channel-Concatenated Network for Single Image Super-ResolutionCode0
MRI Super-Resolution with Ensemble Learning and Complementary Priors—0
Multi-level Wavelet Convolutional Neural NetworksCode0
Distilling with Residual Network for Single Image Super Resolution—0
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Densely Residual Laplacian Super-ResolutionCode0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
Hierarchical Back Projection Network for Image Super-ResolutionCode0
Exemplar Guided Face Image Super-Resolution without Facial LandmarksCode0
On training deep networks for satellite image super-resolution—0
Volumetric Isosurface Rendering with Deep Learning-Based Super-ResolutionCode0
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis—0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Image-Adaptive GAN based ReconstructionCode0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior—0
Hybrid Function Sparse Representation towards Image Super ResolutionCode0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
ODE-Inspired Network Design for Single Image Super-Resolution—0
Residual Networks for Light Field Image Super-Resolution—0
Second-Order Attention Network for Single Image Super-ResolutionCode0
Hyperspectral Image Super-Resolution With Optimized RGB GuidanceCode0
Medical image super-resolution method based on dense blended attention network—0
Ensemble Super-Resolution with A Reference DatasetCode0
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution—0
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks—0
Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-NetCode0
Multi-scale deep neural networks for real image super-resolutionCode0
Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators—0
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks—0
Process of image super-resolution—0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
A Deep Journey into Super-resolution: A surveyCode0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
Evaluating Robustness of Deep Image Super-Resolution against Adversarial AttacksCode0
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-NetworkCode0
Camera Lens Super-ResolutionCode0
Blind Super-Resolution With Iterative Kernel CorrectionCode0
Controlling Neural Networks via Energy Dissipation—0
Deep Back-Projection Networks for Single Image Super-resolutionCode0
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study—0
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model—0
Show:102550
← PrevPage 27 of 32Next →
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