Salt and pepper noise removal is a common inverse problem in image processing. Traditional denoising methods have two limitations. First, noise characteristics are often not described accurately. For example, the noise location information is often ignored and the sparsity of the salt and pepper noise is often described by L1 norm, which cannot illustrate the sparse variables clearly. Second, conventional methods separate the contaminated image into a recovered image and a noise part, thus resulting in recovering an image with unsatisfied smooth parts and detail parts. In this study, we introduce a noise detection strategy to determine the position of the noise, and a non-convex sparsity regularization depicted by Lp quasi-norm is employed to describe the sparsity of the noise, thereby addressing the first limitation. The morphological component analysis framework with stationary Framelet transform is adopted to decompose the processed image into cartoon, texture, and noise parts to resolve the second limitation. Then, the alternating direction method of multipliers (ADMM) is employed to solve the proposed model. Finally, experiments are conducted to verify the proposed method and compare it with some current state-of-the-art denoising methods. The experimental results show that the proposed method can remove salt and pepper noise while preserving the details of the processed image.
翻译:盐和辣椒的噪音去除是图像处理中常见的一个反常问题。传统除尘方法有两个限制。首先,噪音特性往往没有准确描述。例如,噪音定位信息常常被忽略,盐和辣椒噪音的广度常常被L1规范描述,该规范无法清楚地说明稀释变量。第二,传统方法将受污染图像分离成已回收的图像和噪音部分,从而用不满意的光滑部分和细节部分恢复图像。在本研究中,我们采用了噪音探测战略来确定噪音的位置,并且使用Lp准诺姆描述的非convex聚变异性规范来描述噪音的宽度,从而解决第一个限制。采用固定框架变异的形态组成部分分析框架将处理过的图像分解成卡通、纹理和噪音部分,以解决第二个限制。然后,采用乘数的交替方向方法(ADMMM)来解决拟议模式。最后,进行了实验,以核实拟议的方法,并将它与当前状态的噪音调和目前状态的摄氏度规范进行对比,同时,将盐质的摄制结果显示。