【摘要】 针对噪声污染的图像进行有效分割困难问题,提出了一种基于模糊C均值的噪声图像分割方法.该方法首先应用离散小波变换(DWT)将图像进行分解获取不同尺度的小波系数;然后利用粒子群(PSO)算法自适应搜索最优的阈值对小波系数进行处理,将处理好的系数利用小波重构得到重构图像;最后利用模糊C均值聚类完成图像分割.该算法在Berkeley数据集上进行实验,结果表明与其他算法相比,该算法具有更好的性能.更多还原

【Abstract】 Aiming at the problem of effective segmentation of noise-contaminated images, a fuzzy image segmentation method based on fuzzy C-means is proposed. Firstly, the discrete wavelet transform(DWT) is used to decompose the image to obtain wavelet coefficients of different scales. Then the particle swarm optimization(PSO)algorithm is used to adaptively search the optimal threshold to process the wavelet coefficients, and the processed coefficients are processed by wavelet. The reconstructed image is obtained. Finally, the image segmentation is completed by fuzzy C-means clustering. The algorithm is tested on the Berkeley dataset. The experimental results show that the proposed algorithm has better performance than other algorithms.更多还原