Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images
作者：
Highlights：
• Resolution enhancement considering the distortions due to the unstable UAV platforms.
• Analysis of CNN architectures for modeling spatial distribution across spatial scales.
• Investigation of the optimal network parameter settings for the proposed framework.
• Illustration of the advantage of modeling spectral correlation for reconstruction.
• Analysis of perceptual and image space loss functions for resolution enhancement.
摘要
•Resolution enhancement considering the distortions due to the unstable UAV platforms.•Analysis of CNN architectures for modeling spatial distribution across spatial scales.•Investigation of the optimal network parameter settings for the proposed framework.•Illustration of the advantage of modeling spectral correlation for reconstruction.•Analysis of perceptual and image space loss functions for resolution enhancement.
论文关键词：Sub-pixel mapping,Super-resolution,Convolutional neural network,Class distribution,Drone,UAV
论文评审过程：Received 19 June 2017, Revised 22 November 2018, Accepted 27 November 2018, Available online 28 November 2018, Version of Record 12 December 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.11.033
