Convolutional Neural Network (CNN)-Based Fast Back Projection Imaging With Noise-Resistant Capability
Guanqun Sun, Fangzheng Zhang
Abstract
We propose and demonstrate a convolutional neural network (CNN)-based fast back projection (FBP) imaging method, which has noise-resistant capability in strong noise conditions. In this method, the desired high-resolution image is constructed from a low-resolution back projection (BP) image using a pre-trained CNN. Compared to the high-resolution imaging with basic BP algorithm, the proposed CNN-based FBP imaging has significantly reduced complexity, enabling a fast imaging speed. Meanwhile, by training the CNN using noiseless images as the desired output, the CNN-based FBP imaging is noise-resistant, which helps to obtain high-quality images in strong noise scenarios. Performance of this CNN-based FBP imaging method is investigated and compared with basic BP imaging and other methods through extensive numerical simulations. The results show that, using a CNN with optimized structure, the proposed method can greatly improve the imaging speed. Meanwhile, high-quality images with improved peak signal to noise ratios (PSNRs) are obtained in low signal-to-noise-ratio (SNR) conditions. This CNN-based FBP imaging method is expected to find applications where high-quality and fast radar imaging is required.