Two-Dimensional Maximum Entropy Threshold Segmentation Method

Research findings indicate that the two-dimensional maximum entropy threshold segmentation method exhibits excellent noise reduction capabilities, delivering significantly better performance than one-dimensional methods. This approach enables more precise image segmentation while effectively eliminating noise artifacts. Through two-dimensional analysis that considers both pixel intensity and local neighborhood characteristics, we can achieve superior understanding and processing of detailed image information. The algorithm typically involves calculating a 2D histogram of gray levels and their local averages, then determining the optimal threshold by maximizing the entropy criterion in this 2D feature space. Key implementation steps include: constructing a 2D co-occurrence matrix, computing entropy values for different threshold combinations, and selecting the optimal (s,t) threshold pair that maximizes between-class entropy. Therefore, we conclude that the two-dimensional maximum entropy threshold segmentation method serves as an effective noise reduction technique for digital image processing applications.