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 551–600 of 1589 papers

TitleStatusHype
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool—0
Conditioned Regression Models for Non-Blind Single Image Super-Resolution—0
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution—0
Astronomical Image Colorization and upscaling with Generative Adversarial Networks—0
Efficient Super Resolution Using Binarized Neural Network—0
Efficient Super Resolution For Large-Scale Images Using Attentional GAN—0
Compressing Deep Image Super-resolution Models—0
Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution—0
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection—0
Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution—0
A statistically constrained internal method for single image super-resolution—0
Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation—0
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers—0
Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution—0
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices—0
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction—0
Efficient Multi-disparity Transformer for Light Field Image Super-resolution—0
Efficient Model Agnostic Approach for Implicit Neural Representation Based Arbitrary-Scale Image Super-Resolution—0
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks—0
Efficient Learnable Collaborative Attention for Single Image Super-Resolution—0
A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method—0
Efficient Image Super-Resolution via Symmetric Visual Attention Network—0
CMISR: Circular Medical Image Super-Resolution—0
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
CLIP-SR: Collaborative Linguistic and Image Processing for Super-Resolution—0
Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution—0
CLIP-aware Domain-Adaptive Super-Resolution—0
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields—0
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution—0
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks—0
Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey—0
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild—0
Across-scale Process Similarity based Interpolation for Image Super-Resolution—0
Super-Resolution Generative Adversarial Networks based Video Enhancement—0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment—0
Effective Invertible Arbitrary Image Rescaling—0
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction—0
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information—0
E-FCNN for tiny facial expression recognition—0
Characteristic Regularisation for Super-Resolving Face Images—0
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution—0
Advanced Super-Resolution using Lossless Pooling Convolutional Networks—0
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning—0
Channel Splitting Network for Single MR Image Super-Resolution—0
A Review of Deep Learning Based Image Super-resolution Techniques—0
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution—0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution—0
Show:102550
← PrevPage 12 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