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 851875 of 1589 papers

TitleStatusHype
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction0
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution0
GHM Wavelet Transform for Deep Image Super Resolution0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution0
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution0
GRAN: Ghost Residual Attention Network for Single Image Super Resolution0
GuideSR: Rethinking Guidance for One-Step High-Fidelity Diffusion-Based Super-Resolution0
GUN: Gradual Upsampling Network for Single Image Super-Resolution0
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution0
Hierarchical Information Flow for Generalized Efficient Image Restoration0
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution0
Higher-order MRFs based image super resolution: why not MAP?0
High-Frequency aware Perceptual Image Enhancement0
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network0
High Resolution 3D Shape Texture from Multiple Videos0
High-Similarity-Pass Attention for Single Image Super-Resolution0
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution0
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution0
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars0
HIPA: Hierarchical Patch Transformer for Single Image Super Resolution0
HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution0
Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment0
HiT-SR: Hierarchical Transformer for Efficient 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
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