A dictionary learning algorithm for denoising polynomial phase signal based on neural networks.

Under the influence of additive white Gaussian noise, most dictionary learning algorithms whose training data come from the noisy signal cannot effectively remove noise associated with the polynomial phase signal (PPS) via sparse representation, such as K-SVD and RLS-DLA. In this paper, we present a novel dictionary learning algorithm based on neural networks to denoise PPS. In the proposed algorithm, we first use RLS-DLA to train the dictionary. Next, the neural network is used to refine atoms in the learned dictionary. To reduce the computational complexity of iterative calculations in the neural network, direct weight determination of the network is used to denoise atoms. In this way, the signal-to-noise ratio of the reconstructed signal obtained using the proposed algorithm is clearly higher than that of other algorithms, whereas the mean squared error is lower than that of other algorithms. Therefore, the proposed dictionary learning algorithm demonstrates noteworthy denoising performance. [ABSTRACT FROM AUTHOR]