A Side-Lobe Denoise Process for ISAR Imaging Applications: Combined Fast Clean and Spatial Focus Technique

Abstract

The presence of side-lobe noise degrades the image quality and adversely affects the performance of inverse synthetic aperture radar (ISAR) image understanding applications, such as automatic target recognition (ATR), target detection, etc. However, methods reliant on data processing, such as windowing, inevitably encounter resolution reduction, and current deep learning approaches under-appreciate the sparsity inherent in ISAR images. Taking the above analysis into consideration, a convolutional neural network-based process for ISAR side-lobe noise training is proposed in this paper. The proposed processing, based on the ISAR images sparsity characteristic analysis, undergoes enhancements in three core ideas, dataset construction, prior network design, and loss function improvements. In the realm of dataset construction, we introduce a bin-by-bin fast clean algorithm and accelerate the processing speed significantly on the basis of image complete information. Subsequently, a spatial attention layer is incorporated into the prior network designed to augment the network’s focus on the crucial regions of ISAR images. In addition, a loss function featuring a weighting factor is devised to ensure the precise recovery of the strong scattering point. Simulation experiments demonstrate that the proposed process achieves significant improvements in both quantitative and qualitative results over the classical denoise methods.