Multichannel color image denoising based on multiple dictionaries learning

Abstract

Dictionary learning for sparse representation has attracted much attention among researchers in image denoising. However, most dictionary learning-based methods use a single dictionary which has limitation in sparse representation ability. To improve the performance of this methodology, we propose a multichannel color image denoising algorithm based on multiple dictionary learning. Compared with a fixed dictionary, multiple dictionaries have more powerful representation ability. The algorithm first uses a Gaussian mixture model to model the generic patch prior of an external natural color image dataset. Then, the multiple orthogonal dictionaries are initialized with the generic prior by applying singular value decomposition to the covariance matrix of each Gaussian component. The sparse coding coefficients and the multiple dictionaries are alternately updated for better fitting the desired image. Considering the difference of the noise levels in RGB channels, we use a weight matrix to adjust the contributions of different channels for the denoised result. The desired image is estimated based on maximum a posteriori framework. The extensive experiments have demonstrated that our proposed method outperforms some state-of-the-art denoising algorithms in most cases.