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 1201–1250 of 1589 papers

TitleStatusHype
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
Facial Attribute Capsules for Noise Face Super Resolution—0
Generator From Edges: Reconstruction of Facial Images—0
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution—0
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries—0
Learning Deep Analysis Dictionaries for Image Super-Resolution—0
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization—0
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial NetworkCode1
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques—0
Multimodal Deep Unfolding for Guided Image Super-Resolution—0
Learned Multi-View Texture Super-Resolution—0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES—0
HighRes-net: Multi-Frame Super-Resolution by Recursive FusionCode1
Characteristic Regularisation for Super-Resolving Face Images—0
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network—0
Exploiting Style and Attention in Real-World Super-Resolution—0
Scale-wise Convolution for Image RestorationCode1
Adaptive Densely Connected Super-Resolution ReconstructionCode0
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
Spatial-Angular Interaction for Light Field Image Super-ResolutionCode1
Image Processing Using Multi-Code GAN PriorCode0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Explorable Super ResolutionCode0
Texture Hallucination for Large-Factor Painting Super-Resolution—0
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-ResolversCode0
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images—0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss—0
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution—0
Frequency Separation for Real-World Super-ResolutionCode0
Fine-Grained Neural Architecture Search—0
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and ResultsCode0
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing—0
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold DiscriminationCode0
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks—0
A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imagingCode0
AIM 2019 Challenge on Constrained Super-Resolution: Methods and ResultsCode0
Training Set Effect on Super Resolution for Automated Target Recognition—0
Multimodal Image Super-resolution via Deep Unfolding with Side Information—0
Image Super-Resolution via Attention based Back Projection NetworksCode0
Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-ResolutionCode0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
Unsupervised Image Super-Resolution with an Indirect Supervised Path—0
Image Super-Resolution Improved by Edge InformationCode0
Kernel Modeling Super-Resolution on Real Low-Resolution ImagesCode0
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution—0
Unsupervised Projection Networks for Generative Adversarial 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
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