Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising
This addresses the problem of color image denoising for computer vision applications, offering an incremental improvement by extending an existing framework to handle channel-specific noise.
The paper tackles real color image denoising by proposing a multi-channel weighted nuclear norm minimization model that balances noise statistics across RGB channels, achieving superior performance over state-of-the-art methods in experiments on synthetic and real datasets.
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.