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 1151–1200 of 1589 papers

TitleStatusHype
Single Image Super Resolution - When Model Adaptation Matters—0
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images—0
Single-Layer Learnable Activation for Implicit Neural Representation (SL^2A-INR)—0
Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network—0
Single-photon Image Super-resolution via Self-supervised Learning—0
Single-sample image-fusion upsampling of fluorescence lifetime images—0
Single-Step Latent Consistency Model for Remote Sensing Image Super-Resolution—0
Soft-IntroVAE for Continuous Latent space Image Super-Resolution—0
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution—0
Solving Video Inverse Problems Using Image Diffusion Models—0
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks—0
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction—0
Sparse Coding Approach for Multi-Frame Image Super Resolution—0
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints—0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
SPDER: Semiperiodic Damping-Enabled Object Representation—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
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
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
State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications—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
Stroke-based Cyclic Amplifier: Image Super-Resolution at Arbitrary Ultra-Large Scales—0
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference—0
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—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
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
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
Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning—0
Super-resolution Guided Pore Detection for Fingerprint Recognition—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