基于高光谱成像的肉品检测去条带噪声方法

Striping noise removal method in meat detection based on hyperspectral imaging

推扫式高光谱成像系统通常因电荷耦合器件(Charge Coupled Device,CCD)各像元响应不一致而产生条带噪声,该研究以猪肉为研究对象,设计一种基于多块标准反射率板进行逐波段逐像素相对辐射定标的去除条带噪声方法。该方法基于最小二乘法,利用不同反射率标准板的反射率值与CCD输出值,采用多项式拟合出接近于各个波段逐像元真实响应函数的拟合响应函数;选取每一波段图像中的任一像元作为参考,将其余像元与参考像元的拟合响应函数做差,作为其余像元的补偿函数,再采用样本的输出值加补偿值以校正样本。分别采用逐波段逐像素相对辐射定标法和传统矩匹配法对猪肉高光谱图像进行处理,并以四阶标准反射率板各反射区均值与参考值的绝对误差评估反射率校正结果。结果表明,逐波段逐像素相对辐射定法在有效去除条带噪声的同时,不会产生新的噪声,同时能够保持原始图像的灰度分布,校正后图像条带系数最大值比原始图像减少63.5%;相比矩匹配法,反射率误差大幅下降,其中条带系数最大波段(200波段)4个反射区的绝对误差为0.003~0.089,随机抽取波段(300波段)4个反射区的绝对误差为0.009~0.083。该研究为消除条带噪声提供了一种可靠方法,同时为提高后续基于推扫式光谱成像的指标空间可视化精度奠定了良好的基础。

High-resolution hyperspectral imagery has been one of the most fashionable tools using the push broom scanner in motion, particularly for the in-line measurement in industrial production of food and agricultural products. However, a distinct pattern of stripes can tend to impair the image quality that resulted from the non-uniformity among the sensing pixels, due to the inhomogeneity of imaging sensors in manufacturing, dark currents, and working conditions. Taking the pork as the measurement subject, this work aims to devise a de-stripe procedure for the relative radiance calibration using multiple standard reflectance panels. The specific procedure included the illumination of the standard panels to generate spatially uniform-distributed fluxes on the different levels into the camera, in order to gage the response of individual pixels on the imaging sensor. A polynomial fitting of sensor readouts on reflectance values was implemented to profile the response function of individual sensing pixels, and the compensation of all the sensing pixels for each wavelength, according to the response differences to a designated pixel as reference. A reflectance calibration was also provided to calculate the imaging subject reflectance using this procedure, according to the inverse of the fitted response function for the individual sensing pixels. Furthermore, the hyperspectral images of pork were selected to verify the de-stripe performance of the calibration procedure working on the third-order polynomial fitting with that of moment-matching using an intuitive visual evaluation aided with pseudo-color mapping, an objective profiling of the average digital number per column, as well as the maxima of stripe-index. In addition, a step-reflectance standard panel was used to measure the accuracy of the reflectance calibration on the regions of four reflectance values. Results showed that the subject spatial variation across the image columns was dropped along with the removal of stripe-noise, where the apparent pseudo edges were created, especially at the columns located in the transition from background to subject. There was a significant difference in moment matching in the image-columns average digital numbers of the same gray value after calibration. The calibration per individual pixel working on each wavelength substantially reduced the stripe noise, while no new noise was introduced. The spatial distributions in the original image were perfectly maintained as the image-columns average-digital-number profiles after de-stripe calibration, indicating a good agreement with those before. Aside from the compelling visual evidence, the procedure was also quantified in both the maxima of stripe-index after calibration and the lowness of reflectance error. Specifically, there was a 63.5% decrease in the stripe-index maxima on the hyperspectral images of pork. The reflectance calibration error was 0.003-0.089 for the four regions on the step-standard panel in the most stripe-stricken waveband 200, and 0.009-0.083 in a random waveband 300. The procedure of relative radiance calibration and the companion reflectance calibration can be widely expected to reduce the stripe-noise, while for the intact of subjects’ spatial details and precise reflectance values. The finding can be paving the way to better spatial visualization of quality indicators and attributes to the food and agricultural products when working on the images collected using push-broom hyperspectral imagery.