An Integrated Network for SA-ISAR Image Processing With Adaptive Denoising and Super-Resolution Modules

Mingyao Chen, Jingyuan Xia, Tianpeng Liu, Li Liu

Abstract: This letter focuses on developing an effective and generalizable deep learning approach for inverse synthetic aperture radar (ISAR) image super-resolution (SR). Since the ISAR imaging process is typically carried out under sparse aperture (SA) conditions, imaging results may exhibit striped noise caused by echoes missing, making it challenging to apply conventional SR methods directly. In view of this, we present a blind SR (BSR) method specifically designed for ISAR images with striped noise. The proposed method employs an integrated network that includes an adaptive denoising module and a SR module (AD-SRNet). Experimental results on both synthetic and real ISAR samples demonstrate the superior performance and strong generalization capability of our approach.