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 1301–1350 of 1589 papers

TitleStatusHype
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
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—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
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images—0
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network—0
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution—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
Single Image Super Resolution - When Model Adaptation Matters—0
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images—0
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search—0
Single-Layer Learnable Activation for Implicit Neural Representation (SL^2A-INR)—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—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
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
Benchmarking Ultra-High-Definition 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
Bayesian Sparse Representation for Hyperspectral Image Super Resolution—0
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—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
Backdoor Attacks against Image-to-Image Networks—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction—0
A two-stage 3D Unet framework for multi-class segmentation on full resolution image—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