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
Multi-grained Attention Networks for Single Image Super-Resolution—0
Lightweight Image Super-Resolution with Information Multi-distillation NetworkCode1
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery—0
Efficient Residual Dense Block Search for Image Super-ResolutionCode0
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
Underwater Image Super-Resolution using Deep Residual MultipliersCode1
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
Learning Filter Basis for Convolutional Neural Network CompressionCode1
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution—0
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-ResolutionCode0
Image Formation Model Guided Deep 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
Progressive Perception-Oriented Network for Single Image Super-ResolutionCode0
Image Super-Resolution Using a Wavelet-based Generative Adversarial NetworkCode0
Learned Image Downscaling for Upscaling using Content Adaptive ResamplerCode1
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal imagesCode0
Single Image Super-Resolution via CNN Architectures and TV-TV MinimizationCode1
Hybrid Residual Attention Network for Single Image Super ResolutionCode0
Enhanced generative adversarial network for 3D brain MRI super-resolution—0
Gated Multiple Feedback Network for Image Super-ResolutionCode0
FC^2N: Fully Channel-Concatenated Network for Single Image Super-ResolutionCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
MRI Super-Resolution with Ensemble Learning and Complementary Priors—0
Distilling with Residual Network for Single Image Super Resolution—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks—0
Densely Residual Laplacian Super-ResolutionCode0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
Exemplar Guided Face Image Super-Resolution without Facial LandmarksCode0
Hierarchical Back Projection Network for Image Super-ResolutionCode0
On training deep networks for satellite image super-resolution—0
Volumetric Isosurface Rendering with Deep Learning-Based Super-ResolutionCode0
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis—0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Image-Adaptive GAN based ReconstructionCode0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior—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