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 U-net. The U-net is a pixel-wise 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 time-frequency 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.