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 601625 of 1589 papers

TitleStatusHype
Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer LearningCode0
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled DictionariesCode0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
Detail-revealing Deep Video Super-resolutionCode0
Boosting Lightweight Single Image Super-resolution via Joint-distillationCode0
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-ResolutionCode0
Multimodal Sensor Fusion In Single Thermal image Super-ResolutionCode0
Residual Dense Network for Image Super-ResolutionCode0
Densely Residual Laplacian Super-ResolutionCode0
MemNet: A Persistent Memory Network for Image RestorationCode0
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation NetworksCode0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal imagesCode0
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM AgentsCode0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
Blind Super-Resolution With Iterative Kernel CorrectionCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
DeepRED: Deep Image Prior Powered by REDCode0
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution NetworkCode0
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
Lightweight Feature Fusion Network for Single Image Super-ResolutionCode0
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
Deep Mean-Shift Priors for Image RestorationCode0
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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