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 876900 of 1589 papers

TitleStatusHype
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform0
Error-correcting neural networks for semi-Lagrangian advection in the level-set method0
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution0
Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models0
ERQA: Edge-Restoration Quality Assessment for Video Super-ResolutionCode1
In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission0
Boosting Lightweight Single Image Super-resolution via Joint-distillationCode0
Scene Text Image Super-Resolution via Parallelly Contextual Attention NetworkCode0
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale DatasetCode1
ParaDiS: Parallelly Distributable Slimmable Neural Networks0
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning0
A Systematic Survey of Deep Learning-based Single-Image Super-ResolutionCode1
Structure-Preserving Image Super-ResolutionCode1
Resolution based Feature Distillation for Cross Resolution Person Re-Identification0
Toward Real-World Super-Resolution via Adaptive Downsampling Models0
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution0
Exploring Separable Attention for Multi-Contrast MR Image Super-ResolutionCode1
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction0
Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN0
Attention-based Multi-Reference Learning for Image Super-ResolutionCode0
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution0
Transformer for Single Image Super-ResolutionCode1
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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