激光与光电子学进展, 2018, 55 (10): 101103, 网络出版: 2018-10-14   

基于新符号函数与盲源分离的光子计数图像去噪方法

De-Noising Method of Photon Counting Image Based on New Symbol Function and Blind Source Separation
作者单位
山东理工大学电气与电子工程学院, 山东 淄博 255049
摘要
在10-4 lx环境下, 由多像素光子计数探测器利用光子计数原理点阵扫描得到光子计数图像。为了呈现更多细节, 获得更高清晰度的图像, 首先采用Bayes-Shink阈值及改进的新符号函数对光子计数图像进行处理, 然后在图像重构阶段将低频系数置零, 以处理后的高频系数进行图像重构, 并将其设置为虚拟通道, 使观测信号的个数与信号源个数相同, 从而满足快速独立成分分析无噪分离模型, 最后实现光子计数图像和噪声的盲源分离。实验结果表明, 该算法与小波软、硬阈值算法和符号函数算法相比, 图像的峰值信噪比分别提高了16.39%、10.18%、5.20%。同时, 滤除噪声后的图像较好地保护了边缘细节, 视觉效果良好。
Abstract
The photon counting image is scanned by multi-pixel photon counting detector point by point under the environment of 10-4 lx according to the principle of photon counting. To present more details and get a high definition image, the Bayes-Shrink threshold and the improved new symbol functions are used to realize image de-noising preprocessing first. Then, in the stage of image reconstruction, the low-frequency coefficients are set to zero to reconstruct the image with high-frequency coefficients after processing and it is set as a virtual channel to make the number of observation signals equal to the number of signal sources. Finally , the fast independent component analysis noiseless separation model is used to separate the photon counting image from noise by blind source separation. The experimental results show that the peak signal to noise ratios of the image are improved by 16.39%, 10.18%, 5.20%, respectively, compared with the soft, hard and new symbol function de-noising algorithm. The image after removing noise is also good to protect the edge details, and the visual effect is good.

王炫, 尹丽菊, 高明亮, 申晋, 邹国峰, 胡浩东, 仲红玉. 基于新符号函数与盲源分离的光子计数图像去噪方法[J]. 激光与光电子学进展, 2018, 55(10): 101103. Wang Xuan, Yin Liju, Gao Mingliang, Shen Jin, Zou Guofeng, Hu Haodong, Zhong Hongyu. De-Noising Method of Photon Counting Image Based on New Symbol Function and Blind Source Separation[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101103.

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