结合多尺度分解和梯度绝对值算子的显微图像清晰度评价方法

针对显微图像自动对焦和成像系统质量评价问题，结合多尺度分解工具和梯度绝对值算子设计，提出了一种显微图像清晰度评价算法。采用非下采样剪切波分解，对输入的显微图像进行多尺度、多方向变换，得到一幅低频子带图像和若干幅高频子带图像。结合抗噪阈值设置，计算各子带图像的梯度绝对值算子和，利用图像清晰度变化对于低频和高频子带系数影响的差异，将高低频梯度绝对值算子的比值作为最终的显微图像清晰度评价数值。开展了仿真论证实验和实拍论证实验，实验结果表明，所提出的清晰度评价算法具有较好的单调性和抗噪特性，和几种经典的评价算法相比，本文方法得到的评价结果在灵敏度、稳定性和鲁棒性方面表现更为优异，具有很好的实际应用价值。

Micro-image definition evaluation using multi-scale decomposition and gradient absolute value

Aimed at the problem of automatic focus and image system quality evaluation in microscopy imaging, a micro-image definition evaluation method is presented by combining multi-scale decomposition tools and absolute gradient operators. The multiscale and multidirectional non-subsampled Shearlet transform is utilized to decompose the input micro image into a low frequency sub-band image and a number of high frequency sub-band images. Combined with the anti-noise threshold setting, the gradient absolute sum values of each sub-band image were calculated. By using the different effects of image sharpness on the low-frequency and high-frequency sub-band coefficients, the ratio of the high-frequency to low-frequency gradient absolute value operator was taken as the final evaluation value of the microscopic image sharpness. The simulation experiment and actual experiments were carried out and the experimental results illustrated that the proposed approach has good monotonicity and anti-noise characteristics. Compared with other classic evaluation algorithms, the presented method obtained superior performance on sensitivity, stability and robustness. It has very good practical application values.