去除椒盐噪声的交替方向法

Alternating Direction Method for Salt-and-pepper Denoising

传统图像去噪法基于有用信息和噪声频率特性的差别实现去噪,实际中,有用信息和噪声在频带上往往存在重叠,因此,传统去噪法在抑制噪声的同时,往往损失了细节信息,使图像变模糊.本文引入稀疏与低秩矩阵分解模型描述图像去噪问题,基于该模型,采用交替方向法(Alternating direction method,ADM)得到复原图像.实验证明该方法比常用的中值滤波法更有效地抑制了椒盐噪声,同时更好地保持了原始图像的细节信息.

Conventional image denoising algorithms attempt to remove noise on the basis of spectral separation between signal and noise. However, in practice, the spectra of signal and noise usually overlap. As a result, conventional denoising algorithms often suppress noises at the cost of losing details in fine texture and causing blurring in output images. This paper formulates the image denoising problem by adopting a new model of sparse and low-rank matrix decomposition. Based on this model, the alternating direction method （ADM） is utilized to obtain the restored image. Our experiment demonstrates that the ADM suppresses salt-and-pepper noise more efficiently and better preserves detail information in the input image, as compared to the commonly used median filtering method.