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 401425 of 1589 papers

TitleStatusHype
Anchor-based Plain Net for Mobile Image Super-ResolutionCode1
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: ReportCode1
Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical NaturalnessCode1
EDPN: Enhanced Deep Pyramid Network for Blurry Image RestorationCode1
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-ResolutionCode1
Infrared Image Super-Resolution via Transfer Learning and PSRGANCode1
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain ConnectivityCode1
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic ModelsCode1
A Two-Stage Attentive Network for Single Image Super-ResolutionCode1
SRWarp: Generalized Image Super-Resolution under Arbitrary TransformationCode1
Attention in Attention Network for Image Super-ResolutionCode1
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
Image Super-Resolution via Iterative RefinementCode1
Conditional Hyper-Network for Blind Super-Resolution with Multiple DegradationsCode1
Best-Buddy GANs for Highly Detailed Image Super-ResolutionCode1
Flow-based Kernel Prior with Application to Blind Super-ResolutionCode1
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-ResolutionCode1
Designing a Practical Degradation Model for Deep Blind Image Super-ResolutionCode1
Asymmetric CNN for image super-resolutionCode1
A new public Alsat-2B dataset for single-image super-resolutionCode1
Self-Supervised Adaptation for Video Super-ResolutionCode1
Collapsible Linear Blocks for Super-Efficient Super ResolutionCode1
Real-World Single Image Super-Resolution: A Brief ReviewCode1
Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution EnhancementCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
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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