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 1051–1100 of 1589 papers

TitleStatusHype
On-Device Text Image Super Resolution—0
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-ResolutionCode0
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature LearningCode1
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets—0
Dense U-net for super-resolution with shuffle pooling layer—0
EPSR: Edge Profile Super resolution—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution—0
Symmetric Parallax Attention for Stereo Image Super-ResolutionCode1
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction—0
A Novel Fast 3D Single Image Super-Resolution Algorithm—0
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image RestorationCode1
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference—0
Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network—0
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Attaining Real-Time Super-Resolution for Microscopic Images Using GANCode1
WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution—0
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-ResolutionCode0
Efficient Image Super-Resolution Using Pixel AttentionCode1
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
FAN: Frequency Aggregation Network for Real Image Super-resolution—0
Interpretable Detail-Fidelity Attention Network for Single Image Super-ResolutionCode1
Deep Artifact-Free Residual Network for Single Image Super-Resolution—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
Tarsier: Evolving Noise Injection in Super-Resolution GANsCode1
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results—0
Residual Feature Distillation Network for Lightweight Image Super-ResolutionCode1
GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain ConnectomesCode1
AdderSR: Towards Energy Efficient Image Super-Resolution—0
Parallax Attention for Unsupervised Stereo Correspondence LearningCode1
AIM 2020 Challenge on Efficient Super-Resolution: Methods and ResultsCode2
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results—0
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter EstimationCode1
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission—0
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-ResolutionCode1
Interpretable Deep Multimodal Image Super-Resolution—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Image Super-Resolution using Explicit Perceptual Loss—0
MDCN: Multi-scale Dense Cross Network for Image Super-ResolutionCode1
Multi-Attention Based Ultra Lightweight Image Super-ResolutionCode1
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
Deep Variational Network Toward Blind Image RestorationCode1
Cascade Convolutional Neural Network for Image Super-Resolution—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
E-FCNN for tiny facial expression recognition—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