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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 noiseresistant, which helps to obtain high-quality images in strong noise scenarios. Performance of this CNNbased 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. INDEX TERMS Synthetic aperture radar, back projection algorithm, fast back projection imaging, convolutional neural network, high-resolution imaging.

Photonics-based high-resolution 3D radar imaging is demonstrated in which a convolutional neural network (CNN)-assisted back projection (BP) imaging method is applied to implement fast and noise-resistant image construction. The proposed system uses a 2D radar array with each element being a broadband radar transceiver realized by microwave photonic frequency multiplication and mixing. The CNN-assisted BP image construction is achieved by mapping low-resolution images to high-resolution images with a pre-trained 3D CNN, which greatly reduces the computational complexity and enhances the imaging speed compared with basic BP image construction. Besides, using noise-free or low-noise ground truth images for training the CNN, the CNN-assisted BP imaging method can suppress the noises, which helps to generate high-quality images. In the experiment, 3D radar imaging with a K-band photonics-based radar having a bandwidth of 8 GHz is performed, in which the imaging speed is enhanced by a factor of ∼55.3 using the CNN-assisted BP imaging method. By comparing the peak signal to noise ratios (PSNR) of the generated images, the noise-resistant capability of the CNN-assisted BP method is soundly verified.

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