SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 2361–2370 of 3874 papers

TitleStatusHype
Seirios: Leveraging Multiple Channels for LoRaWAN Indoor and Outdoor Localization—0
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution—0
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-ResolutionCode1
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image RescalingCode1
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
Efficient Light Field Reconstruction via Spatio-Angular Dense NetworkCode0
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning—0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Del-Net: A Single-Stage Network for Mobile Camera ISP—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified