高毅, 于瀛, 杨絮, 项双喜. 基于多波段光源的智能痕迹分色方法[J]. 应用光学, 2023, 44(1): 46-54. DOI: 10.5768/JAO202344.0101007
引用本文: 高毅, 于瀛, 杨絮, 项双喜. 基于多波段光源的智能痕迹分色方法[J]. 应用光学, 2023, 44(1): 46-54. DOI: 10.5768/JAO202344.0101007
GAO Yi, YU Ying, YANG Xu, XIANG Shuangxi. Intelligent trace color separation method based on multi-band light source[J]. Journal of Applied Optics, 2023, 44(1): 46-54. DOI: 10.5768/JAO202344.0101007
Citation: GAO Yi, YU Ying, YANG Xu, XIANG Shuangxi. Intelligent trace color separation method based on multi-band light source[J]. Journal of Applied Optics, 2023, 44(1): 46-54. DOI: 10.5768/JAO202344.0101007

基于多波段光源的智能痕迹分色方法

Intelligent trace color separation method based on multi-band light source

  • 摘要: 针对常规分色摄影技术难以获得清晰可辨的图像信息和丰富的纹理特征,设计了一种基于多波段光源的痕迹分色摄影智能系统。通过STM32、LED、加色效应、互补色原理设计多波段光源模块,解决不同痕迹背景对应单色光的选择问题;通过混合式步进电机、DEV8825电机驱动模块和STM32,设计可上下、左右移动的光源模块,解决最佳配光位置的寻找问题;通过改进的FS-SIFT配准算法和引入二代Curvelet融合算法,设计两者相结合的图像处理系统,解决了常规摄影中痕迹提取与图像处理未同步的问题。实验结果表明,该系统摄影的痕迹图像,其背景与痕迹间的亮度反差更大,质量更好;经改进的FS-SIFT算法配准及二代Curvelet算法融合处理后,图像标准差平均提升1.25倍,信息熵平均提升1.70倍,平均梯度平均提升1.46倍,所得图像平均信息量和纹理特征更丰富。

     

    Abstract: For the conventional color separation photography technology was difficult to obtain the clear image information and rich texture characteristics, a trace color separation photography intelligent system based on multi-band light source was designed. The multi-band light source module was designed by STM32, LED, additive color effect and complementary color principle to solve the problem of selecting monochromatic light corresponding to different trace backgrounds. The light source module that could be moved up and down, left and right was designed by hybrid stepper motor, DEV8825 motor driver module and STM32 to solve the problem of finding the best light distribution position. Through the improved FS-SIFT registration algorithm and the introduction of the second-generation Curvelet fusion algorithm, a combined image processing system was designed to solve the problem of unsynchronized trace extraction and image processing in conventional photography. The experimental results show that the brightness contrast between the background and the trace image photographed by the system is larger and the quality is better. After the improved FS-SIFT algorithm registration and the second-generation Curvelet algorithm fusion processing, the average standard deviation of the image is increased by 1.25 times, the average information entropy is increased by 1.70 times, the average gradient is increased by 1.46 times, and the average amount of information and texture features of the obtained image are richer.

     

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