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Lv Yang, Ning Yu, Ma Haotong, et al. Research on computationally adaptive plenoptic imaging[J]. Opto-Electronic Engineering, 2018, 45(3): 180075. doi: 10.12086/oee.2018.180075
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Research on computationally adaptive plenoptic imaging
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Abstract
As for computational adaptive plenoptic imaging system, the light-field of the target and interference are measured together, and then according to distribution characteristics of the four-dimensional light-field information between the target and the disturbed factors, target and disturbed factors can be effectively separated. This technique can be used to detect and recover the wavefront distortion caused by interference in the large field of view, and adaptively compensate for complicated wavefront aberration by means of computation. Compared with the traditional adaptive optics imaging method, the proposed method has a larger detecting field of view, and can directly analyze and compute wavefront information based on the extended target.-
Keywords:
- adaptive optics /
- computational imaging /
- plenoptic imaging /
- plenoptic camera
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References
[1] Clare R M, Lane R G. Phase retrieval from subdivision of the focal plane with a lenslet array[J]. Applied Optics, 2004, 43(20): 4080-4087. doi: 10.1364/AO.43.004080 [2] Clare R M. Comparison of wavefront sensing using subdivision at the aperture and focal planes[J]. Proceedings of SPIE, 2004: 1211-1222. [3] Esposito S, Pinna E, Puglisi A, et al. Pyramid sensor for segmented mirror alignment[J]. Optics Letters, 2005, 30(19): 2572-2574. doi: 10.1364/OL.30.002572 [4] Esposito S, Riccardi A. Pyramid wavefront sensor behavior in partial correction adaptive optic systems[J]. Astronomy & Astrophysics, 2001, 369(2): L9-L12. [5] Rodríguez J M, Femenía B, Montilla I, et al. The CAFADIS camera: a new tomographic wavefront sensor for Adaptive Optics[C]// Adaptative Optics for Extremely Large Telescopes, 2010. [6] Rodríguez-Ramos J M, Magdaleno Castelló E, Domínguez Conde C, et al. 2D-FFT implementation on FPGA for wavefront phase recovery from the CAFADIS camera[J]. Proceedings of SPIE, 2008, 7015: 701539. doi: 10.1117/12.789312 [7] Rodríguez-Ramos J M, Femenía Castellá B, Pérez Nava F, et al. Wavefront and distance measurement using the CAFADIS camera[J]. Proceedings of SPIE, 2008, 7015: 70155Q. [8] Eduardo M, Manuel R, Manuel R R J. An Efficient Pipeline Wavefront Phase Recovery for the CAFADIS Camera for Extremely Large Telescopes[J]. Sensors, 2010, 10(1): 1. doi: 10.1109/JSEN.2009.2039287 [9] Rodríguez-Ramos L F, Montilla I, Lüke J P, et al. Atmospherical wavefront phases using the plenoptic sensor (real data)[J]. Proceedings of the SPIE, 2012, 8384: 83840D. [10] Rodríguez-Ramos L F, Montilla I, Fernández-Valdivia J J, et al. Concepts, laboratory, and telescope test results of the plenoptic camera as a wavefront sensor[J]. Proceedings of SPIE, 2012, 8447: 844745. [11] Trujillo-Sevilla J M, Fernandez-Valdivia J J, Marichal-Hernandez J G, et al. Plenoptic deconvolution in turbulent scenarios[C]//Information Optics, IEEE, 2014: 1-3. http://ieeexplore.ieee.org/document/6933286 [12] Wu C, Ko J, Davis C C. Imaging through turbulence using a plenoptic sensor[C]//Oceans. International Society for Optics and Photonics, 2015. http://spie.org/Publications/Proceedings/Paper/10.1117/12.2190975 [13] 张锐, 杨金生, 田雨, 等.焦面哈特曼传感器波前相位复原[J].光电工程, 2013, 40(2): 32-39. Zhang R, Yang J S, Tian Y, et al. Wavefront Phase Recovery from the Plenoptic Camera[J]. Opto-Electronic Engineering, 2013, 40(2): 32-39. [14] 许洁平, 梁永辉, 蒋鹏志.光场相机波前传感器性能分析[J].光学学报, 2014, 34(B12): 201001. Xu J P, Liang Y H, Jiang P Z. Performance analysis of light field wave-front sensor[J]. Acta Optica Sinica, 2014, 34(B12): 201001. -
Overview
Overview: For the complicated imaging environment with turbulent atmosphere or obstacle interference, the imaging performance of the optics imaging system will seriously decrease. In order to improve the imaging resolution of the complicated imaging system, post image processing and adaptive optics techniques are always utilized. As for post image processing, it has a special condition for image collecting environment, sampling rate and the pre-information of the image, thus it has a large computation load and is difficult to realize real time or near real time processing. Though the traditional adaptive optics technique can detect and compensate for the wavefront distortion information caused by environment, it cannot deal with the problem of anisoplanatic and large field of view applications. The system is complicated, expensive and hard to control, so that it is not available for small equipment. Furthermore, it is a correction system based on the optical field wavefront phase difference, and because of the complicated imaging environment, the obstacles located on the optical propagation pass may modulate the optical field of the target, which results in lacking of target plenoptic information and cannot obtain wavefront distortion of the extended target being partially occluded. It means that the imaging system cannot obtain clear image of the target by adaptive correction and the traditional adaptive optics is not available in the case of complicated occluded imaging environment.
In this article, computational optical imaging technology is introduced to the application of adaptive optics imaging method, based on the advantage of computational optical imaging system. Different from the traditional adaptive optics imaging based on phase conjugation, the computational adaptive plenoptic imaging system is aimed to decrease the amplitude and phase interference caused by the complicated environment based on the computational imaging correlation method. As for computational adaptive plenoptic imaging system, the light-field of the target and obstacle are measured together, and then according to distribution characteristics of the four-dimensional optical field information between the target and the obstacle, target and obstacle can be effectively separated. On one hand, this technique can be used to detect and recover the wavefront distortion caused by interference in the large field of view, and adaptively compensate for complicated wavefront aberration by means of computation. On the other hand, it can delete the certain effect caused by the obstacle performing to the target optical field in the complicated environment. Compared with the traditional adaptive optics imaging method, the proposed method has a larger detecting field of view, and can directly analyze and compute wavefront information based on the extended target. The computational plenoptic imaging system has no active optical equipment or dynamic equipment. It utilizes the computational method instead of mechanical deformable mirror to realize phase compensation, and can adaptively compensate for the complicated wavefront phase perturbation in the imaging space. It has the advantages of compact structure and low cost. Furthermore, it can delete the interferential imaging effect from the obstacle located in the optical pass of higher dimensional optical field to obtain clear images.
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Lv Yang, Ning Yu, Ma Haotong, et al. Research on computationally adaptive plenoptic imaging[J]. Opto-Electronic Engineering, 2018, 45(3): 180075. doi: 10.12086/oee.2018.180075
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Figure 1.
Computationally adaptive plenoptic imaging configuration
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Figure 2.
Phase screens used in numerical simulation. (a) Defocus phase screen; (b) Atmosphere turbulence phase screen
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Figure 3.
Blurred images captured by imaging CCD with phase aberrations. (a) With defocus phase aberration; (b) With atmosphere turbulence phase aberration
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Figure 4.
Intensity distribution of images captured by plenoptic detector CCD with phase aberrations. (a) With defocus phase aberration; (b) With atmosphere turbulence phase aberration
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Figure 5.
Phase errors of the wavefront distortion restoration. (a) Defocus phase aberration restoration; (b) Atmosphere turbulence phase aberration restoration. The RMS phase errors of defocus aberration and atmosphere turbulence aberration restoration are 0.038λ and 0.063λ, respectively
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Figure 6.
Corresponding reconstructed near-diffraction-limited images. (a) Disturbed by defocus phase aberration; (b) Disturbed by atmosphere turbulence phase aberration
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Figure 7.
Defocus aberration generated in the experiment and its corresponding blurred image. (a) Defocus aberration; (b) Blurred image
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Figure 8.
Plenoptic image and its corresponding restored phase distribution. (a) Plenoptic image; (b) Restored phase distribution
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Figure 9.
Reconstructed near-diffraction-limited image and phase error of wavefront distortion restortion. (a) Reconstructed near-diffraction-limited image; (b) Phase error. The RMS phase error of aberration restoration is 0.0187λ
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Figure 10.
The experimental results of computationally plenoptic imaging of the launching tower 4 kilometers away from the experimental system. (a) Blurred image captured by traditional imaging CCD; (b) Reconstructed image
