升余弦变增益微视觉图像自适应增强与应用

微视觉系统中同轴光源和光学衍射的存在, 使CCD相机获取的图像具有灰度值偏低、光照不均匀、动态范围大、对比度差以及微细结构丢失或无法辨识的缺陷。为改善图像质量, 本文提出一种升余弦变增益子带分解微视觉图像自适应增强方法。该算法首先基于图像特性利用自适应Log增益对原图像进行增强, 提高微视觉图像中亮暗区细节特征与背景的对比度; 接着使用自适应升余弦卷积进行快速照度估计; 然后对各通道的输出图像采用自适应变增益子带分解算法获取独立光谱子带; 最后进行亮度校正、图像融合与色彩恢复。将该算法用于微位移测量系统中可使测量结果的相对误差小于20%; 用于处理光照不均的图像可有效降低同轴光源靠近中心区域的亮度; 此外, 扩展至普通图像的处理中可提高对比度, 改善细节特征。3组实验结果的平均图像质量相对提高率为81.46%, 71.18%和93.75%; 平均耗时为386 s, 0.24 s和1.27 s。

Abstract

The micro-vision images captured by the CCD camera typically show low gray value, non-uniform illumination, large dynamic range, poor contrast and missed or unrecognized fine structure due to the coaxial light source and optical diffraction in micro-vision system. Based on the raised cosine and variable gain sub-band decomposition algorithm, a micro-vision image adaptive enhancement method was developed to improve the image quality in this work. First, on the basis of image characteristic, the original micro-vision image was enhanced by using adaptive Log gain function to improve the contrast between minutiae and background which located in bright or dark regions. Then, the fast illumination estimation was carried out in terms of the adaptive cosine convolution. After that, the independent spectral sub-bands of the image in each channel were obtained through the adaptive variable gain sub-band decomposition algorithm. The ultimately enhanced image was generated after the implement of intensity correction, image fusion and color restoration. The algorithm had been successfully applied to a micro displacement measurement system and the relative error was less than 20%. Moreover, extended the algorithm to ordinary image processing can improve the contrast and minutiae. The relative increasing rates of the average quality of three experiments were 81.46%, 71.18% and 93.75%, respectively; and the average consuming time were 3.86 s, 0.24 s, and 1.27 s, respectively.

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张翔, 张宪民, 李海, 李凯. 升余弦变增益微视觉图像自适应增强与应用[J]. 光学精密工程, 2018, 26(5): 1254. ZHANG Xiang, ZHANG Xian-min, LI Hai, LI Kai. Micro-vision image adaptive enhancement and application based on raised cosine variable gain[J]. Optics and Precision Engineering, 2018, 26(5): 1254.

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