去除椒盐噪声的交替方向法

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.

Alternating Direction Method for Salt-and-pepper Denoising

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.