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 901–950 of 1589 papers

TitleStatusHype
A statistically constrained internal method for single image super-resolution—0
Trainable Loss Weights in Super-ResolutionCode0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?Code0
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution—0
Deep Residual Axial Networks—0
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution—0
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening FrameworkCode0
Content-Aware Local GAN for Photo-Realistic Super-Resolution—0
Toward Accurate Post-Training Quantization for Image Super ResolutionCode0
MSRA-SR: Image Super-resolution Transformer with Multi-scale Shared Representation Acquisition—0
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective—0
Spectral Bayesian Uncertainty for Image Super-Resolution—0
Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis—0
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid NetworkCode0
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels—0
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images—0
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach—0
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution—0
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information—0
MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution—0
Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory—0
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution—0
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution—0
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture—0
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution—0
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
RDRN: Recursively Defined Residual Network for Image Super-Resolution—0
Super-resolution Reconstruction of Single Image for Latent features—0
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
A Comprehensive Survey of Transformers for Computer Vision—0
Contrastive Learning for Climate Model Bias Correction and Super-Resolution—0
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection—0
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Iris super-resolution using CNNs: is photo-realism important to iris recognition?—0
How Real is Real: Evaluating the Robustness of Real-World Super Resolution—0
Boomerang: Local sampling on image manifolds using diffusion models—0
Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer—0
Real Image Super-Resolution using GAN through modeling of LR and HR process—0
ITSRN++: Stronger and Better Implicit Transformer Network for Continuous Screen Content Image Super-Resolution—0
ISTA-Inspired Network for Image Super-Resolution—0
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks—0
CUF: Continuous Upsampling Filters—0
Deep Fourier Up-SamplingCode0
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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