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 1101–1150 of 1589 papers

TitleStatusHype
Robust Unpaired Single Image Super-Resolution of Faces—0
Rolling Shutter Super-Resolution—0
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection—0
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images—0
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks—0
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution—0
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization—0
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
Self-Organized Residual Blocks for Image Super-Resolution—0
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution—0
Self-Tuned Deep Super Resolution—0
Semantically Accurate Super-Resolution Generative Adversarial Networks—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution—0
Semantic Segmentation Using Super Resolution Technique as Pre-Processing—0
Sequence Matters: Harnessing Video Models in 3D Super-Resolution—0
Seven ways to improve example-based single image super resolution—0
Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network—0
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation—0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning—0
Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution—0
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network—0
Simultaneous Super-Resolution of Depth and Images Using a Single Camera—0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior—0
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder—0
Single Image Super-Resolution—0
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss—0
Single Image Super-Resolution Based on Global-Local Information Synergy—0
Single Image Super-Resolution based on Wiener Filter in Similarity Domain—0
Single image super-resolution by approximated Heaviside functions—0
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission—0
Single Image Super-Resolution From Transformed Self-Exemplars—0
Single image super resolution in spatial and wavelet domain—0
Single Image Super-Resolution Methods: A Survey—0
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels—0
Single Image Super-Resolution Using Lightweight CNN with Maxout Units—0
Single Image Super-resolution using Deformable Patches—0
Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer—0
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network—0
Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction—0
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network—0
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network—0
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis—0
Single Image Super Resolution via Manifold Approximation—0
Single Image Super-Resolution via Residual Neuron Attention Networks—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
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—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