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 876–900 of 1589 papers

TitleStatusHype
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-ResolutionCode0
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-ResolutionCode0
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging—0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
A High-Frequency Focused Network for Lightweight Single Image Super-Resolution—0
Parameter-Free Channel Attention for Image Classification and Super-Resolution—0
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images—0
Synthesizing Realistic Image Restoration Training Pairs: A Diffusion Approach—0
Raising The Limit Of Image Rescaling Using Auxiliary Encoding—0
CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
Combination of Single and Multi-frame Image Super-resolution: An Analytical PerspectiveCode0
Single-photon Image Super-resolution via Self-supervised Learning—0
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution—0
GRAN: Ghost Residual Attention Network for Single Image Super Resolution—0
TextIR: A Simple Framework for Text-based Editable Image Restoration—0
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution—0
DISCO: Distributed Inference with Sparse Communications—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales—0
TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
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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
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