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 576600 of 1589 papers

TitleStatusHype
GRAN: Ghost Residual Attention Network for Single Image Super Resolution0
TextIR: A Simple Framework for Text-based Editable Image Restoration0
Spatially-Adaptive Feature Modulation for Efficient Image Super-ResolutionCode2
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuningCode1
DISCO: Distributed Inference with Sparse Communications0
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression0
Improving Scene Text Image Super-resolution via Dual Prior Modulation NetworkCode1
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales0
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-ResolutionCode1
TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation0
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function SpaceCode1
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution0
Hyperspectral Image Super Resolution with Real Unaligned RGB GuidanceCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
OSRT: Omnidirectional Image Super-Resolution with Distortion-aware TransformerCode1
A statistically constrained internal method for single image super-resolution0
Image Restoration with Mean-Reverting Stochastic Differential EquationsCode2
Trainable Loss Weights in Super-ResolutionCode0
Image Super-Resolution using Efficient Striped Window TransformerCode2
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?Code0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution0
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution0
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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
8SwinFIRPSNR29.36Unverified
9CPAT+PSNR29.36Unverified
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