马明阳,
赵首博,
1.哈尔滨理工大学测控技术与通信工程学院 哈尔滨 150080
2.哈尔滨理工大学测控技术与仪器黑龙江省高校重点实验室 哈尔滨 150080
基金项目:国家自然科学基金(61801148, 61803128)，黑龙江省自然科学基金(QC2016067)
详细信息
作者简介:范剑英：男，1963年生，教授，硕士生导师，研究方向为光电检测、数字图像与重建
马明阳：男，1993年生，硕士生，研究方向为压缩感知与数字信号处理
赵首博：男，1985年生，副教授，硕士生导师，研究方向为精密光电测量、计算视觉成像
通讯作者:赵首博 shoubozh@126.com
中图分类号:TN911.73; TN957.52计量
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被引次数:0
出版历程
收稿日期:2019-07-09
修回日期:2020-01-17
网络出版日期:2020-02-17
刊出日期:2020-06-04
Research on High Reflective Imaging Technology Based on Compressed Sensing
Jianying FAN,Mingyang MA,
Shoubo ZHAO,
1. School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
2. Measurement and Control Technology and Instrument Key Laboratory of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China
Funds:The National Natural Science Foundation of China (61801148, 61803128), The Scientific Research Foundation of Heilongjiang Province (QC2016067)
摘要
摘要:高反光物体成像时反射的光强容易超出传感器接收光强的最大量化值，使得采集图像部分区域图像失真，严重影响信息传递。为了改善高反光成像饱和区域中数据丢失的状况，该文结合压缩感知这一新的采样理论提出基于压缩感知高反光成像方法，利用特定测量矩阵对目标图像进行线性采样，将CCD图像传感器的单个光强采样值与测量矩阵中的分布数据对应结合，对整合后的数据用算法进行恢复重建实现被测目标在高光环境中成像。以峰值信噪比和灰度直方图作为客观评定标准。实验表明，该成像方法鲁棒性较强、可行性较高，直方图检测饱和像素占比为0%，峰值信噪比为58.37 dB实现了在高光环境下不含饱和光成像，为压缩感知在成像应用中提供了新的方向。
关键词:高反光成像/
压缩感知/
数据整合/
直方图
Abstract:When imaging a highly reflective object, the light intensity reflected easily exceeds the maximum quantized value of the light intensity received by the sensor, which causes image distortion of the captured image in the saturated region of light intensity and seriously affects the quality of information transmission. In order to improve the data loss in the high-reflection imaging saturation region, a compression-sensing of high-reflection imaging method based on the new sampling theory of compressed sensing is proposed. A specific measurement matrix is used to conduct linear sampling of the target image, and the single light intensity sampling value of the CCD image sensor is combined with the distribution data in the measurement matrix, and the integrated data is restored and reconstructed with the algorithm to achieve the imaging of the measured target in the high-light environment. The peak signal to noise ratio and gray histogram are used as objective evaluation criteria. Experiments show that this imaging method is robust and feasible, with the proportion of saturated pixels in histogram detection 0% and the peak signal to noise ratio 58.37 dB, realizing the imaging without saturated light in the high-light environment, providing a new direction for the application of compressed sensing in imaging.
Key words:High reflected light imaging/
Compressed sensing/
Data integration/
Histogram
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