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 1341–1350 of 3874 papers

TitleStatusHype
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution—0
Automated Symbolic Law Discovery: A Computer Vision Approach—0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results—0
Generalized Matrix-Pencil Approach to Estimation of Complex Exponentials with Gapped Data—0
OFDM Reference Signal Pattern Design Criteria for Integrated Communication and Sensing—0
DeepDPM: Dynamic Population Mapping via Deep Neural Network—0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network—0
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results—0
Generalizable One-shot Neural Head Avatar—0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified