Parameters Optimization for Impulse Noise Suppressing: A Deep Learning Based Approach

Impulse noise has always been a significant topic in the area of signal processing, especially for noise mitigation algorithms. In this paper, a time-domain denoising method is combined with deep learning, whose parameters are calculated by neural networks. By constructing a proper training set, trained neural networks can avoid the large cost of traversal and resolve the problem of fixed parameters. Regarding the fact that the computing power of terminal devices is usually insufficient, the noise suppressing method is expected to be with low complexity and achieve superior effects. Simulation results further validate that neural networks have favorable performance in predicting parameters for the denoising algorithm.