Fei LIU, Xiaoqin WU, Jingbo DUAN, Pingli HAN, Xiaopeng SHAO. An Introduction of Application of Computational Imaging in Photoelectric Detection（Invited）[J]. Acta Photonica Sinica, 2021, 50(10): 1011001
- Acta Photonica Sinica
- Vol. 50, Issue 10, 1011001 (2021)
Fig. 1. Schematic diagram of computational imaging link
Fig. 2. Computational imaging link and classification
Fig. 3. Principle of wavefront shaping based on feedback［16］
Fig. 4. Image result with incoherent light［18］
Fig. 5. Scattering imaging based on optical TM［19］
Fig. 6. Experimental results in complex scattering medium［24］
Fig. 7. Scattering imaging base on OPC［25］
Fig. 8. The imaging of 2.5 cm isolated chicken breast tissue［28］
Fig. 9. Schematic diagram of SBSC imaging principle［32］
Fig. 10. Rotation tracking results of different objects［39］
Fig. 11. Active NLOS imaging based on streak tube camera［41］
Fig. 12. NLOS imaging principle and reconstruction result with obstruction［47］
Fig. 13. NLOS imaging of spatially coherent［48］
Fig. 14. Shape recovery from coherence measurements［48］
Fig. 15. NLOS imaging of intensity-coherent［49］
Fig. 16. Reconstruction results of different hidden scenes［49］
Fig. 17. Reconstruction results of character［51］
Fig. 18. The dehazing results in the real scene［58］
Fig. 19. Imaging result and evaluation curve［61］
Fig. 20. Comparison of imaging results［66］
Fig. 21. Restoration results of different targets［69］
Fig. 22. Comparison of restoration results in water with different turbidity［70］
Fig. 23. Normal vector of microfacet［71］
Fig. 24. The principle of polarization 3D imaging［72］
Fig. 25. Reconstruction results of different target objects［75］
Fig. 26. Reconstruction results of different target objects［79］
Fig. 27. 3D reconstruction results in different environments［80］
Fig. 28. Reconstruction results of colored cartoon plaster targe［81］
Fig. 29. Principle of multi-aperture imaging［82］
Fig. 30. Multi-aperture system prototype and imaging results［94］
Fig. 32. Multi-scale computational optical imaging system and its imaging effect［105］
Fig. 33. Comparison of traditional design and global design［107］
Fig. 34. Comparison of restoration image of traditional design and joint design［107］
Fig. 35. The image quality between the joint design of three lenses and the traditional design of six lens［116］
Fig. 36. Correction of system chromatic by optical-algorithm design method［117］
Fig. 37. Infrared reconstruction results of targets in different scenes［119］
Fig. 38. Convolutional neural network model［120］
Fig. 39. Comparison of reconstruction results［120］
Fig. 40. EDSR restoration results［122］
Fig. 41. Decomposition principle of low-rank and sparse matrix［123］
Fig. 42. Recovery result of LRSD-TNN［124］
Fig. 43. Face restoration results of LRSD-TNN［124］
Fig. 44. Detection results of infrared dim and small targets［125］
Fig. 45. Reconstruction results of the original image under different concentration［126］
Fig. 46. The defogging results in different scenes［127］
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