基于人工蜂群与模糊C均值的自适应小波变换的噪声图像分割

针对传统模糊C均值（FCM）聚类算法在处理噪声图像时易受到噪声影响的问题，提出了基于FCM的小波域特征增强的噪声图像分割方法。首先，将噪声图像进行二维小波分解；其次，对近似系数进行边缘增强，同时利用人工蜂群（ABC）优化算法对细节系数进行阈值处理，并将处理后的系数进行小波重构；最后，对重构后的图片使用FCM算法来进行图像分割。选取5幅典型的灰度图像，分别添加高斯噪声和椒盐噪声，使用多种方法进行分割，以分割后图像的峰值信噪比（PSNR）和误分率（ME）作为性能指标，实验结果表明，所提方法分割后的图片相较于传统FCM聚类算法分割方法和粒子群优化（PSO）分割方法分割后的图片在PSNR上最多分别有281%和54%的提升，在ME上最多分别有55%和41%的降低。可见所提出的分割方法较好地保留了图像边缘纹理信息，其抗噪性能与分割性能得到了提升。

Noise image segmentation by adaptive wavelet transform based on artificial bee swarm and fuzzy C-means

Aiming at the problem that traditional Fuzzy C-Means (FCM) clustering algorithm is easily affected by noise in processing noise images, a noise image segmentation method of wavelet domain feature enhancement based on FCM was proposed. Firstly, the noise image was decomposed by two-dimensional wavelet. Secondly, the approximate coefficient was enhanced at the edge, and Artificial Bee Colony (ABC) optimization algorithm was used to perform threshold processing to the detail coefficients, and then the wavelet reconstruction was carried out for the processed coefficients. Finally, the reconstructed image was segmented by FCM algorithm. Five typical grayscale images were selected, and were added with Gaussian noise and salt-and-pepper noise respectively. Various methods were used to segment them, and the Peak Signal-to-Noise Ratio (PSNR) and Misclassification Error (ME) of the segmented images were taken as performance indicators. Experimental results show that the PSNR of the images segmented by the proposed method is at most 281% and 54% higher than the PSNR of the images segmented by the traditional FCM clustering algorithm segmentation method and Particle Swarm Optimization (PSO) segmentation method respectively, and the segmented images of the proposed method has the ME at most 55% and 41% lower than those of the comparison methods respectively. It can be seen that the proposed segmentation method preserves the edge texture information well, and the anti-noise and segmentation performance of this method are improved.