CAJ | 학술논문

针对现有算法无法有效解决混合噪声图像的阈值分割的问题，该文提出3维最小误差阈值法。该方法充分考虑图像像元点的灰度分布信息及像元点之间的灰度相关信息，结合图像灰度、均值和中值信息，构造出3维观测空间。然后基于相对熵定义出3维最佳阈值判别式。为了提高该算法的处理速度，给出相应的快速递推算法，其时间复杂度为O(L3)。实验结果表明，在不同噪声环境及非均匀光照条件下，尤其对混合噪声图像，与现有方法相比，文中算法均取得了更好的分割效果。
침대현유산법무법유효해결혼합조성도상적역치분할적문제，해문제출3유최소오차역치법。해방법충분고필도상상원점적회도분포신식급상원점지간적회도상관신식，결합도상회도、균치화중치신식，구조출3유관측공간。연후기우상대적정의출3유최가역치판별식。위료제고해산법적처리속도，급출상응적쾌속체추산법，기시간복잡도위O(L3)。실험결과표명，재불동조성배경급비균균광조조건하，우기대혼합조성도상，여현유방법상비，문중산법균취득료경호적분할효과。
The threshold segmentation of mixed noise image can not be solved by existing algorithms efficiently. A 3D minimum error thresholding algorithm is proposed. Using gray distribution information of pixels and relevant information of neighboring pixels, it combines information of image gray, mean and median to construct a three-dimensional observation space, and then defines a 3D optimal threshold discriminant based on the relative entropy. Furthermore, in order to improve its processing speed, the fast recursive formulas are also given. Its time complexity is O(L3). Experimental results show that the proposed algorithm outperforms those 2D thresholding methods not only for different types of noised image, but also for non-uniform illuminating images. Especially for mixed noise image, its advantage is more obvious.