【摘要】 根据顺序形态变换的相关概念和性质,提出了一种新的图像增强算法。该算法通过对图像做局部加权均值滤波,得到图像增强的基值分量;采用多方位结构元素与图像边缘匹配,计算图像关于各个方位结构元素的加权均值并选取其中的最大值来确定边缘;将此最大值与基值分量之差作为增强分量来扩大图像灰度梯度的动态范围;针对图像中的高灰度区和灰度剧变区,应用图像局部均值和方差自适应调节增强系数。因此,算法在抑制图像中的高频噪声的同时,能有效提升图像中的边缘和目标。实验结果表明,增强前后图像标准差由41.1515,36.9133提高到62.0535,52.8331;图像熵由15.8463,16.8998减少到15.8156,16.8324。更多还原

【Abstract】 According to correlative conception and properties of order morphology transformation, a novel image enhancement algorithm is proposed. With this algorithm, by performing local weighted average value filtering to obtain the basic component for image enhancement and image edge matching with structural elements of different orientations, the weighted average values of structural elements of different orientations are calculated for distinguishing edge and rejecting noises. The difference between the maximum among these weighted average values and basic value is used as an enhancement value for enlarging dynamic scope of image gradient. For regions with high grey-scale level or with tensely variable grey level, the image enhancement coefficients shall be adaptively controlled based on local mean value and variance. As a result, the target and edge of image are sharpened while high frequency noises of image are restrained. Experimental results show that after image being enhanced, the standard deviation of original image increases from 41.1515 and 36.9133 to 62.0535 and 52.8331, entropy from 15.8463 and 16.8998 to 15.8156 and 16.8324 thus improving the contrast of image.更多还原