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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1451–1500 of 1589 papers

TitleStatusHype
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Generic Perceptual Loss for Modeling Structured Output Dependencies—0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution—0
Generic 3D Convolutional Fusion for image restoration—0
Generator From Edges: Reconstruction of Facial Images—0
GRAN: Ghost Residual Attention Network for Single Image Super Resolution—0
Generative Powers of Ten—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution—0
GuideSR: Rethinking Guidance for One-Step High-Fidelity Diffusion-Based Super-Resolution—0
GUN: Gradual Upsampling Network for Single Image Super-Resolution—0
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution—0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results—0
Generative Adversarial Networks for Image Super-Resolution: A Survey—0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
Text-guided Explorable Image Super-resolution—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
TextIR: A Simple Framework for Text-based Editable Image Restoration—0
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution—0
Hierarchical Information Flow for Generalized Efficient Image Restoration—0
GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention—0
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution—0
GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution—0
Higher-order MRFs based image super resolution: why not MAP?—0
High-Frequency aware Perceptual Image Enhancement—0
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results—0
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance—0
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution—0
High Resolution 3D Shape Texture from Multiple Videos—0
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme—0
High-Similarity-Pass Attention for Single Image Super-Resolution—0
Texture-Based Error Analysis for Image Super-Resolution—0
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution—0
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution—0
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars—0
HIPA: Hierarchical Patch Transformer for Single Image Super Resolution—0
HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution—0
Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment—0
Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution—0
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution—0
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
Texture Hallucination for Large-Factor Painting Super-Resolution—0
Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution—0
How Real is Real: Evaluating the Robustness of Real-World Super Resolution—0
HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution—0
Show:102550
← PrevPage 30 of 32Next →
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