Learning Based Noise Identification Techniques Using TimeFrequency Analysis and the UNet In wireless communication, it is inevitable that the signal is highly affected by the noise. For example, for the radar located in the sea shore, due to the effects of sea clutter and remote detection range, the signal to noise ratio (SNR) is only about 015 dB. In this manuscript, we develop an advanced noise determination and removal algorithm based on the deep learning method of the Unet. The Unet is a pixelwise classification network and widely used in image segmentation. In this work, we find that it is also an effective way to determine whether a pixel in the timefrequency domain is the signal part or the noise part, even in the low SNR case. It is very helpful for reducing the noise effect and improving the accuracy of fundamental frequency analysis for radar signal processing.

A Fast Iterative Method for Removing Sparse Noise from Sparse Signals Reconstructing a signal corrupted by impulsive noise is of high importance in several applications, including impulsive noise removal from images, audios and videos, and separating texts from images. Investigating this problem, in this paper we propose a new method to reconstruct a noisecorrupted signal where both signal and noise are sparse but in different domains. We apply our algorithm for impulsive noise (Salt andPepper Noise (SPN) and RandomValued Impulsive Noise (RVIN)) removal from images and compare our results with other notable algorithms in the literature. Simulation indicates show that our algorithm is not only simple and fast, but also it outperforms the other stateoftheart methods in terms of reconstruction quality andor complexity.