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

Showing 426450 of 1589 papers

TitleStatusHype
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity AttackCode1
Exploiting Raw Images for Real-Scene Super-ResolutionCode1
Deep Burst Super-ResolutionCode1
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-ResolutionCode1
3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstructionCode1
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous DatasetsCode1
Deep Learning-based Face Super-Resolution: A SurveyCode1
Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGANCode1
Memory-Efficient Hierarchical Neural Architecture Search for Image RestorationCode1
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution NetworksCode1
Neural Radiance Flow for 4D View Synthesis and Video ProcessingCode1
Hierarchical Residual Attention Network for Single Image Super-ResolutionCode1
Bayesian Image Reconstruction using Deep Generative ModelsCode1
Learning Spatial Attention for Face Super-ResolutionCode1
Pre-Trained Image Processing TransformerCode1
Fully Quantized Image Super-Resolution NetworksCode1
Rank-One Network: An Effective Framework for Image RestorationCode1
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature LearningCode1
Symmetric Parallax Attention for Stereo Image Super-ResolutionCode1
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image RestorationCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Attaining Real-Time Super-Resolution for Microscopic Images Using GANCode1
Efficient Image Super-Resolution Using Pixel AttentionCode1
Interpretable Detail-Fidelity Attention Network for Single Image Super-ResolutionCode1
Tarsier: Evolving Noise Injection in Super-Resolution GANsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
10CPATPSNR29.34Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16Unverified
2HMA†PSNR28.13Unverified
3Hi-IR-LPSNR28.13Unverified
4HAT-LPSNR28.09Unverified
5HAT_FIRPSNR28.07Unverified
6CPAT+PSNR28.06Unverified
7DRCTPSNR28.06Unverified
8HATPSNR28.05Unverified
9CPATPSNR28.04Unverified
10SwinFIRPSNR28.03Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72Unverified
2DRCT-LPSNR28.7Unverified
3HMA†PSNR28.69Unverified
4HAT-LPSNR28.6Unverified
5HAT_FIRPSNR28.43Unverified
6DRCTPSNR28.4Unverified
7HATPSNR28.37Unverified
8CPAT+PSNR28.33Unverified
9CPATPSNR28.22Unverified
10PFTPSNR28.2Unverified