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在结构光三维扫描测量中，强反射表面因编码结构光照射后易产生局部镜面反射的特性，引起相机曝光饱和，淹没了所要检测的表面几何特征信息.为解决强反射表面的视觉成像难题，基于数字微镜器件（digital micromirror device，DMD）具有调制入射光线空间信息的特性，本文提出一种基于DMD的自适应高动态范围成像方法.设计与搭建了一套新型可编程的计算成像系统，建立其光学系统模型，并实现了DMD微镜与CMOS像素的匹配与映射；分析了基于逐像素编码曝光的高动态范围成像原理，并设计了基于DMD的光强编码控制算法，实现对入射光线强度的自适应精确调制，从而使进入成像系统中的入射光强始终处于相机的合适曝光强度内.实验表明：该方法突破了普通数字相机的动态范围限制，能够精确地控制被测强反射表面各个区域的入射光线强弱，并实现了对强反射表面的局部过曝光区域的清晰成像.该研究成果将为从根源上解决强反射表面因局部过曝光造成的三维点云缺失问题提供重要的解决方案.In the three-dimensional (3D) scanning measurement based on structured light techniques, the strong reflection surface is easy to produce local specular reflection due to the illumination of the structured light, which will cause the camera to be over-exposed, and therefore the geometry information of strong reflection surface cannot be detected. Since the digital micromirror device (DMD) has the modulating characteristics of the spatial information of incident light, an adaptive high-dynamic-range imaging method based on DMD is proposed to solve the problem of visual imaging of strong reflection surface. Firstly, a novel and computational imaging system is designed and built, and its optical model is also established. Then, the matching and mapping methods between DMD micromirrors and CMOS pixels are described in detail and realized. Meanwhile, we analyze the theory of the high-dynamic-range imaging based on per-pixel coded exposure, and design a coding control algorithm of light intensity to achieve the adaptive precision modulation of the intensity of incident light, so that the incident light in the imaging system is always in appropriate exposure intensity. The experiments show that the method can break through the limited dynamic range of the ordinary digital camera, and accurately control the intensity of incident light in each region of the measured strong reflection surfaces, and thus it can obtain the high-quality images of the local over-exposure area of the strong reflection surface. More importantly, the research will provide a new solution to the problem of 3D point cloud loss caused by local over-exposure of the strong reflection surface.
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[17] Li M F, Mo X F, Zhao L J, Huo J, Yang R, Li K, Zhang A N 2016 Acta Phys. Sin. 65 064201 (in Chinese)[李明飞, 莫小范, 赵连洁, 霍娟, 杨然, 李凯, 张安宁 2016 物理学报 65 064201]
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[1] Srikantha A, Sidibé D 2012 Signal Process. 27 650
[2] Wang C, Tu C 2014 Int. J. Signal Process. Image Process. Pattern Recognit. 7 217
[3] Gu B, Li W, Wong J, Zhu M, Wang M 2012 J. Visual Commun. Image Represent. 23 604
[4] Venkataraman K, Jabbi A S, Mullis R H 2015 US Patent 9 041 829[2015-05-26]
[5] Ward G J, Seetzen H, Heidrich W 2012 US Patent 8 242 426[2012-08-14]
[6] Brajovic V 2004 Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Washington DC, USA, June 2 2004 p189
[7] Lai L W, Lai C H, King Y C 2004 IEEE Sens. J. 4 122
[8] Ikebe M, Saito K 2007 IEEE Sens. J. 7 897
[9] Zhou C, Nayar S K 2011 IEEE Trans. Image Proc. 20 3322
[10] Mannami H, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y 2007 J. Visual Commun. Image Represent. 18 359
[11] Li X, Sun C, Wang P 2015 Opt. Lasers Eng. 66 41
[12] Yang Z, Wang P, Li X, Sun C 2014 Opt. Lasers Eng. 54 31
[13] Dudley D, Duncan W M, Slaughter J 2003 Proc. SPIE-The International Society for Optical Engineering USA, January 20 2003 Vol. 4985
[14] Zhang H, Cao L, Jin G 2017 Appl. Opt. 56 F138
[15] Cheng J, Gu C, Zhang D, Wang D, Chen S C 2016 Opt. Lett. 41 1451
[16] Qiao Y, Xu X, Liu T, Pan Y 2015 Appl. Opt. 54 60
[17] Li M F, Mo X F, Zhao L J, Huo J, Yang R, Li K, Zhang A N 2016 Acta Phys. Sin. 65 064201 (in Chinese)[李明飞, 莫小范, 赵连洁, 霍娟, 杨然, 李凯, 张安宁 2016 物理学报 65 064201]
[18] Feng W, Zhang F, Wang W, Xing W, Qu X 2017 Appl. Opt. 56 3831
[19] Li L Z, Yao X R, Liu X F, Yu W K, Zhai G J 2014 Acta Phys. Sin. 63 224201
[20] Feng W, Zhang F, Qu X, Zheng S 2016 Sensors 16 331
[21] Ri S, Fujigaki M, Matui T, Morimoto Y 2006 Appl. Opt. 45 6940
[22] Mackie C J, Candian A, Huang X, Lee T J, Tielens A 2015 J. Chem. Phys. 142 244107
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