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 1351–1400 of 1589 papers

TitleStatusHype
A Boosting Method to Face Image Super-resolution—0
SPDER: Semiperiodic Damping-Enabled Object Representation—0
Attention-based Image Upsampling—0
Spectral Bayesian Uncertainty for Image Super-Resolution—0
SpectraLift: Physics-Guided Spectral-Inversion Network for Self-Supervised Hyperspectral Image Super-Resolution—0
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution—0
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution—0
SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation—0
SphereSR: 360deg Image Super-Resolution With Arbitrary Projection via Continuous Spherical Image Representation—0
SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution—0
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling—0
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging—0
ATGV-Net: Accurate Depth Super-Resolution—0
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network—0
SRFeat: Single Image Super-Resolution with Feature Discrimination—0
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network—0
A Study in Dataset Pruning for Image Super-Resolution—0
SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments—0
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution—0
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution—0
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging—0
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution—0
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network—0
Astronomical Image Colorization and upscaling with Generative Adversarial Networks—0
A statistically constrained internal method for single image super-resolution—0
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers—0
State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications—0
Video Super-Resolution Using a Grouped Residual in Residual Network—0
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention—0
StereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representation—0
Stochastic Deep Restoration Priors for Imaging Inverse Problems—0
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction—0
Stroke-based Cyclic Amplifier: Image Super-Resolution at Arbitrary Ultra-Large Scales—0
A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method—0
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference—0
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields—0
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—0
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information—0
A Review of Deep Learning Based Image Super-resolution Techniques—0
Structure Tensor Based Image Interpolation Method—0
Exploiting Style and Attention in Real-World Super-Resolution—0
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging—0
A Reverse Hierarchy Model for Predicting Eye Fixations—0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs—0
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution—0
Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators—0
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution—0
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning—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