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
- Acta Optica Sinica
- Vol. 39, Issue 12, 1228001 (2019)
Fig. 1. Schematic of whiskbroom scanning imaging of space camera
Fig. 2. Distortion process of target image
Fig. 3. Calculation of pixel number of nadir ground scene perpendicular to track
Fig. 4. Schematic of corresponding pixels of distorted and extended images perpendicular to track
Fig. 5. Schematic of width of imaging area along track
Fig. 6. Schematics of corresponding pixels of distorted and extended images along track. (a) Original distorted image; (b) extended image
Fig. 7. Architecture of SRCNN
Fig. 8. Architecture of wbi-SRCNN
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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Table 1. Parameters for on-orbit imaging and ground simulation
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Table 2. NR-IQA results of test images
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