Acta Optica Sinica, Volume. 39, Issue 12, 1228001(2019)
Inversion Restoring Algorithm for Whiskbroom Scanning Images Synthesized with Deep Convolutional Neural Network
Fig. 4. Schematic of corresponding pixels of distorted and extended images perpendicular to track
Fig. 6. Schematics of corresponding pixels of distorted and extended images along track. (a) Original distorted image; (b) extended image
Fig. 9. Schematic of experimental device of satellite whiskbroom scanning imaging
Fig. 10. Distortion correction result of whiskbroom scanning image. (a) Ground truth; (b) simulated whiskbroom scanning image; (c) distortion-corrected whiskbroom scanning image
Fig. 11. Distortion correction result of whiskbroom scanning linear target. (a) Simulated whiskbroom scanning linear target; (b) distortion-corrected whiskbroom scanning linear target
Fig. 12. Enhanced results of distortion-corrected whiskbroom scanning images. (a) Distortion-corrected whiskbroom scanning images; (b) result of Bicubic; (c) result of SRCNN; (d) result of wbi-SRCNN. Left is wall and right is roof
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Chao Xu, Guang Jin, Xiubin Yang, Tingting Xu, Lin Chang. Inversion Restoring Algorithm for Whiskbroom Scanning Images Synthesized with Deep Convolutional Neural Network[J]. Acta Optica Sinica, 2019, 39(12): 1228001
Category: Remote Sensing and Sensors
Received: Jun. 13, 2019
Accepted: Aug. 8, 2019
Published Online: Dec. 6, 2019
The Author Email: Yang Xiubin (yangxiubin@ciomp.ac.cn)