Deng Hongyao, Zhu Qingxin, Song Xiuli, Tao Jinsong
School of Information & Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
College of Computer Engineering, Yangtze Normal University, Chongqing 408000, China.
Comput Intell Neurosci. 2017;2017:2024396. doi: 10.1155/2017/2024396. Epub 2017 Apr 27.
Impulsive noise removal usually employs median filtering, switching median filtering, the total variation method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by PSNR/SSIM results as well as the visual quality of restored images.
脉冲噪声去除通常采用中值滤波、切换中值滤波、总变差方法及其变体。然而，这些方法常常会引入过度平滑，可能导致大量视觉特征模糊，因此仅适用于低密度噪声的图像。本文提出了一种新的噪声去除方法来克服这一局限性，该方法根据不同的噪声特征将像素分为不同类别。如果图像被椒盐噪声破坏，像素被分为受损像素和无噪声像素；如果图像被随机值脉冲破坏，像素被分为受损像素、无噪声像素和可能受损像素。落入不同类别的像素将进行不同的处理。如果一个像素受损，则应用改进的总变差扩散；如果该像素可能受损，则应用加权总变差扩散；否则，该像素保持不变。实验结果表明，该方法对不同的噪声强度具有鲁棒性，适用于不同的图像，PSNR/SSIM结果以及恢复图像的视觉质量表明其具有强大的噪声去除能力。
