基于多尺度形态学大豆图像滤波方法

Method for smoothing soybean image noise based on multi-scale morphology

提出了一种基于多尺度形态学滤波算法,用于减少噪声影响从而提高大豆图像质量。首先用多尺度结构元素分别对原图像进行开闭重建运算,构造形态学开闭塔;然后计算相邻尺度形态学开闭重建图像间的差,构造亮特征和暗特征的差异塔;最后根据不同尺度的亮特征和暗特征重建图像。通过一组被不同噪声污染的大豆图像验证该文提出的滤波算法,并采用一些标准的评估方法将该文方法与文中提到的其它滤波方法在不同噪声污染情况下进行比较,试验结果表明本文方法的滤波效果优于其它方法。

A method for improving the quality of soybean images by reducing the effect of noise using multi-scale morphology is presented in this paper. First, multi-scale structure element was used to make open-and-close reconstructing operation to source image to construct morphological open-and-close towers; then different operations of morphological open-and-close reconstruction images between adjacent scales were computed to construct different towers of bright features and dark features. According to bright features and dark features of different scales, smoothed image was reconstructed. The proposed method has been tested on a set of soybean images corrupted with different types of noise. The results were compared with some other standard noise removal algorithms based on some standard performance measures. The results show that the method proposed in this paper is superior to others.