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

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Showing 1401–1450 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-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
Multi-scale Residual Network for Image Super-ResolutionCode0
SRFeat: Single Image Super-Resolution with Feature Discrimination—0
ESRGAN: Enhanced Super-Resolution Generative Adversarial NetworksCode3
Learning Sparse Low-Precision Neural Networks With Learnable Regularization—0
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 Consistency—0
An Attention-Based Approach for Single Image Super Resolution—0
Image Super-Resolution Using Very Deep Residual Channel Attention NetworksCode2
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis—0
CT-image Super Resolution Using 3D Convolutional Neural Network—0
Non-Local Recurrent Network for Image RestorationCode0
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network—0
Feature Super-Resolution: Make Machine See More Clearly—0
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior—0
Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution—0
A hybrid approach of interpolations and CNN to obtain super-resolution—0
Multi-level Wavelet-CNN for Image RestorationCode0
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-ResolutionCode0
Image Super-Resolution via Dual-State Recurrent NetworksCode0
Large Receptive Field Networks for High-Scale Image Super-Resolution—0
Densely Connected High Order Residual Network for Single Frame Image Super Resolution—0
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution—0
An efficient CNN for spectral reconstruction from RGB imagesCode1
A two-stage 3D Unet framework for multi-class segmentation on full resolution image—0
Reference-Conditioned Super-Resolution by Neural Texture Transfer—0
A Fully Progressive Approach to Single-Image Super-ResolutionCode0
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature TransformCode3
Task-Driven Super Resolution: Object Detection in Low-resolution Images—0
Fast and Accurate Single Image Super-Resolution via Information Distillation NetworkCode0
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction—0
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual NetworkCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
Deep Back-Projection Networks For Super-ResolutionCode0
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected NetworkCode0
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network—0
Residual Dense Network for 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
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—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