红外与激光工程, 2017, 46 (8): 0824001, 网络出版: 2017-11-07  

多孔径压缩编码超分辨率大视场成像方法

Multi-aperture super-resolution and wide-field imaging method using compressive coding
作者单位
西安电子科技大学 物理与光电工程学院, 陕西 西安 710071
摘要
多孔径成像是一种融合了仿生复眼视觉的新型成像方法, 具有小型化、大视场、高分辨率等多种优势, 但由于每个子孔径对应的单元图像分辨率过低, 导致其成像质量和视场角的提升十分有限。为了进一步提高成像分辨率和探测视场, 基于压缩感知理论设计随机编码模板, 并紧贴子孔径放置对入射光场进行调制, 通过单次曝光记录编码后的低分辨率单元图像阵列, 利用稀疏优化算法, 重构所有低分辨率单元图像获得超分辨率大视场图像。理论分析和仿真实验证明了该方法的有效性。该方法不仅能兼顾大视场高分辨率成像, 而且大大缩小系统等效焦距, 具有薄层结构, 体积小而重量轻, 可为微光机电一体化系统的研制设计提供借鉴。
Abstract
Multi-aperture imaging is a new imaging method combining with compound eye concept, which has a small size, large field of view, high-resolution images reconstruction and other advantages. However, due to the low resolution of sub-images, the improvements for the image resolution and field of view are very limited. A novel imaging method which could achieve both super-resolution and large field of view was proposed. The random coded mask was designed based on the framework of compressive sensing and placed on each sub-aperture. Instead of directly imaging and converging on the image sensor, the incident light field of each sub-aperture would be modulated by the coded mask. Then, the random projections of the input object could be acquired by the low-dimension image sensor within a single exposure. Finally, the sparse representation-based optimization algorithm was applied to reconstruct super-resolution and large field of view images from all low-resolution sub-images, which had more object pixels than the number of pixels of the image sensor. Both the theoretical model and simulation results show the feasibility of the proposed method. Moreover, this method greatly reduces system equivalent focal length and has a thin structure, which can provide theoretical guidance for the design and application of the micro-optical electromechanical system.
参考文献

[1] 巩宪伟, 鱼卫星, 张红鑫,等. 仿生复眼成像系统设计与制作的研究进展[J]. 中国光学, 2013, 6(01):34-45.

    Gong Xianwei, Yu Weixing, Zhang Hongxin, et al. Progress in design and fabrication of artificial compound eye optical systems[J]. Chinese Optics, 2013, 6(1): 34-45. (in Chinese)

[2] 王小蕾, 王克逸, 曹兆楼,等. 目标定位仿生复眼视觉系统成像位置计算[J]. 红外与激光工程, 2013, 42(12): 3433-3439.

    Wang Xiaolei, Wang Keyi, Cao Zhaolou, et al. Location of the target image for compound eye system[J]. Infrared & Laser Engineering, 2013(1): 36-38. (in Chinese)

[3] Tanida J, Kumagai T, Yamada K, et al. Thin observation module by bound optics (TOMBO): an optoelectronic image capturing system[C]//SPIE, 2000, 4086(11): 1030-1036.

[4] Tanida J, Kumagai T, Yamada K, et al. Thin observation module by bound optics (TOMBO): concept and experimental verification[J]. Applied Optics, 2001, 40(11): 1806-13.

[5] Kanaev A V, Ackerman J R, Fleet E F, et al. TOMBO sensor with scene-independent superresolution processing[J]. Optics Letters, 2007, 32(19): 2855-7.

[6] Fife K, Gamal A E, Wong H S P. A 3MPixel multi-aperture image sensor with 0.7 μm pixels in 0.11 μm CMOS[C]//Solid-State Circuits Conference, 2008. ISSCC 2008. IEEE International, 2008: 48-594.

[7] Shankar M, Willett R, Pitsianis N P, et al. Ultra-thin multiple-channel LWIR imaging systems[C]//SPIE, 2006, 6294: 629411.

[8] Shankar M, Willett R, Pitsianis N. Thin infrared imaging systems through multichannel sampling [J]. Applied Optics, 2008, 47(10): 1-10.

[9] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

[10] Donoho D L, Tsaig Y. Extensions of compressed sensing[J]. Signal Processing, 2006, 86(3): 533-548.

[11] Maro F Duarte, Mark A Davenport, Dharmpal Takhar, et al. Signal pixel imaging via compressive sampling[J]. IEEE Sig Pro Mag, 2008, 25(2): 83-91.

[12] Marcia R, Harmany Z, Willett R. Compressive coded apertures for high-resolution imaging[C]//SPIE, 2010, 7723: 772304.

[13] Marcia R F, Harmany Z T, Willett R M. Compressive coded aperture imaging [C]//IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2009, 7246: 72460G.

[14] Marcia R F, Willett R M. Compressive coded aperture super-resolution image reconstruction[C]//Pro of IEEE International Conference on Acoustics, Speech and Signal Processing, 2008: 833-836.

[15] 肖龙龙, 刘昆, 韩大鹏, 等. 焦平面编码高分辨率红外成像方法[J]. 红外与激光工程, 2011, 40(11): 2065-2070.

    Xiao Longlong, Liu Kun, Han Dapeng, et al. Focal plane coding method for high resolution infrared imaging [J]. Infrared & Laser Engineering, 2011, 40(11): 2065-2070. (in Chinese)

[16] 邓承志, 田伟, 汪胜前, 等. 近似稀疏正则化的红外图像超分辨率重建[J]. 光学 精密工程, 2014, 22(6): 1648-1654.

    Deng Chengzhi, Tian Wei, Wang Shengqian, et al. Super-resolution reconstruction of approximate sparsity regularized infrared images [J]. Optics & Precision Engineering, 2014, 22(6):1648-1654. (in Chinese)

[17] 王朋, 荣志斌, 何俊华, 等. 基于压缩感知的偏振光成像技术研究[J]. 红外与激光工程, 2016, 45(2): 274-280.

    Wang Peng, Rong Zhibin, He Junhua, et al. Polarization imaging based on compressed sensing theory [J]. Infrared & Laser Engineering, 2016, 45(2): 274-280. (in Chinese)

[18] 陈健, 高慧斌, 王伟国,等. 超分辨率复原方法相关原理研究[J]. 中国光学, 2014, 7(6): 897-910.

    Chen Jian, Gao Huibin, Wang Weiguo, et al. Correlation theory of super-resolution restoration method [J]. Chinese Optics, 2014, 7(6): 897-910. (in Chinese)

[19] Candes E J. The restrictes isometry property and its implications for compresses sesing [J]. Academics, 2006, 346(1): 598-592.

[20] Haupt J, Nowak R. Signal reconstruction from noisy random projections [J]. IEEE Trans on Information Theory, 2006, 52(9): 4036-4048.

[21] 李珅, 马彩文, 李艳,等. 压缩感知重构算法综述[J]. 红外与激光工程, 2013, 42(S1): 225-232.

    Li Shen, Ma Caiwen, Li Yan, et al. Survey on reconstruction algorithm based on compressive sensing [J]. Infrared & Laser Engineering, 2013, 42(S1): 225-232. (in Chinese)

[22] Fiqueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems [J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(14): 586-598.

[23] Bajwa W, Haupt J, Raz G, et al. Toeplitz-structured compressed sensing matrices [C]//Pro of IEEE SP 14th Workshop on Statistical Signal, 2007: 294-298.

袁影, 王晓蕊, 吴雄雄, 穆江浩, 张艳. 多孔径压缩编码超分辨率大视场成像方法[J]. 红外与激光工程, 2017, 46(8): 0824001. Yuan Ying, Wang Xiaorui, Wu Xiongxiong, Mu Jianghao, Zhang Yan. Multi-aperture super-resolution and wide-field imaging method using compressive coding[J]. Infrared and Laser Engineering, 2017, 46(8): 0824001.

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