Most of the existing denoising algorithms are developed for grayscale images,
while it is not a trivial work to extend them for color image denoising because
the noise statistics in R, G, B channels can be very different for real noisy
images. In this paper, we propose a multi-channel (MC) optimization model for
real color image denoising under the weighted nuclear norm minimization (WNNM)
framework. We concatenate the RGB patches to make use of the channel
redundancy, and introduce a weight matrix to balance the data fidelity of the
three channels in consideration of their different noise statistics. The
proposed MC-WNNM model does not have an analytical solution. We reformulate it
into a linear equality-constrained problem and solve it with the alternating
direction method of multipliers. Each alternative updating step has closed-form
solution and the convergence can be guaranteed. Extensive experiments on both
synthetic and real noisy image datasets demonstrate the superiority of the
proposed MC-WNNM over state-of-the-art denoising methods.

本研究提出了一种基于多通道优化模型的加权核范数最小化方法进行真实彩色图像去噪的算法，该方法采用权重矩阵平衡三个通道的数据保真度并引入可用于通道冗余的 RGB 贴片，通过交替方向乘子法解决该 MC-WNNM 模型，实验结果表明该算法在合成和真实噪声图像数据集上优于现有的去噪方法。