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 801–850 of 1589 papers

TitleStatusHype
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution—0
FAN: Frequency Aggregation Network for Real Image Super-resolution—0
Fast and Accurate Image Upscaling With Super-Resolution Forests—0
Fast Image Super-Resolution Based on In-Place Example Regression—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Fast Single Image Super-Resolution—0
Fast single image super-resolution based on sigmoid transformation—0
Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution—0
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution—0
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces—0
Feature Super-Resolution: Make Machine See More Clearly—0
Federated Learning for Blind Image Super-Resolution—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
Fine-Grained Neural Architecture Search—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Fingerprints of Super Resolution Networks—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution—0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures—0
Fourier Space Losses for Efficient Perceptual Image Super-Resolution—0
FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution—0
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution—0
FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform—0
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network—0
Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution—0
Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution—0
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution—0
GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution—0
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention—0
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
Generative Adversarial Networks for Image Super-Resolution: A Survey—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Generative Powers of Ten—0
Generator From Edges: Reconstruction of Facial Images—0
Generic 3D Convolutional Fusion for image restoration—0
Generic Perceptual Loss for Modeling Structured Output Dependencies—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