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 1251–1300 of 1589 papers

TitleStatusHype
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
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images—0
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
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
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing—0
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and ResultsCode0
Fine-Grained Neural Architecture Search—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
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-ResolutionCode0
Unsupervised Image Super-Resolution with an Indirect Supervised Path—0
Image Super-Resolution Improved by Edge InformationCode0
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution—0
Kernel Modeling Super-Resolution on Real Low-Resolution ImagesCode0
Unsupervised Projection Networks for Generative Adversarial Networks—0
Multi-grained Attention Networks for Single Image Super-Resolution—0
Efficient Residual Dense Block Search for Image Super-ResolutionCode0
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery—0
Enhancing Traffic Scene Predictions with Generative Adversarial Networks—0
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme—0
Unsupervised Learning for Real-World Super-Resolution—0
Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision—0
Edge-Informed Single Image Super-ResolutionCode0
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors—0
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-ResolutionCode0
Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations—0
Robust Regression via Deep Negative Correlation Learning—0
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors—0
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution—0
Image Formation Model Guided Deep Image Super-ResolutionCode0
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-ResolutionCode0
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution—0
Multi-Contrast Super-Resolution MRI Through a Progressive Network—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
Content and Colour Distillation for Learning Image Translations with the Spatial Profile LossCode0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
Improved Super-Resolution Convolution Neural Network for Large Images—0
Learning Resolution-Invariant Deep Representations for Person Re-Identification—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