表面微小疵病在用不同波长的光照明时,其光学图像会存在不同程度的非线性放大畸变现象,对成像信息中的目标提取和信噪比(SNR)将产生一定的影响。提出利用红、绿、蓝三基色光源辐照下的疵病图像进行动态彩色编码成像的方法,经像素级图像融合后可实现高冗余信息的图像合成,从而在获取表面微小疵病丰富细节的同时可进一步提高信噪比。分析了针对三基色图像进行彩色编码的理论,提出了基于图像梯度的动态权值融合成像方法,同时给出了光谱非线性放大信号的噪声分析模型,通过理论分析和数值模拟两方面,充分验证了动态权值融合降低标准偏差的有效性。在明场表面疵病成像实验中,采用三基色平衡响应的彩色互补金属氧化物半导体(CMOS)相机,分别获取了单个微米量级表面疵病点的三基色滤波图像,并通过与传统边缘提取、非动态权值组合等方法的结果比对,验证该优化方法可得到一幅细节更为丰富的高SNR图像。在暗场表面疵病成像实验中,以图像灰度平均梯度和提取到的疵病数量为评价参数,即从优化图像质量和疵病识别能力两个方面,进一步验证该优化在高保真和低噪声方面的有效性。提出的动态彩色编码融合成像方法可有效降低光谱非线性放大带来的噪声。

When the surface tiny defects are illuminated by the light with different wavelengths, their optical image exist nonlinear and amplified distortion phenomenon with different degrees. It has certain effects on target extraction and signal-to-noise ratio (SNR) of the imaging information. An approach that defect image illuminated by red, green, and blue light source is used for dynamic spectral coding imaging is proposed. The image synthesis with high redundancy can be realized through pixel-level image fusion. Then it can further enhance SNR while fetching rich details of micro defects on the surface. The spectral coding theory is analyzed based on three-primary-color images, and then the dynamic weight fusion imaging method is proposed based on the image gradient. The corresponding noise analysis model of spectral nonlinear amplified signal is given. The effectiveness of dynamic weight fusion reducing standard deviation is fully verified by both theoretical analysis and numerical simulation. In the bright field surface defect imaging experiment, color complementary metal oxide silicon (CMOS) camera of three-primary-color equilibrium response is adopted to obtain three-primary-color filtering image of single micron-size defect point on the surface. By comparing with the results of traditional edge extraction, non-dynamic weight combination, and other methods, it is proved that the proposed optimization method can get a high SNR image with richer details. In the dark field surface defect imaging experiment, image gray level mean gradient and extracted defect quantity as evaluation parameters, namely two aspects of optimizing image quality and defect recognition ability, are used to futher verify the effectiveness of high fidelity and low noise. The study of this dynamic spectral coding fusion imaging method can effectively reduce the noises from spectral nonlinear amplification.