Deblurring of radar images aided by point spread function estimator and convolutional regulariser

An innovative deep learning-based solution to address motion blur in radar images, resulting from radar platform or target movement, is presented in this work. Leveraging Convolutional Neural Network (CNN), the proposed method learns a mapping from blurred to deblurred images, while a separate CNN estimates the point spread function (PSF) of the motion blur. This estimated PSF is then used to reconstruct deblurred images, optimising the reconstruction process by integrating the input image, estimated PSF, and ground truth relationship into the training loss term. Trained on a comprehensive dataset of simulated blurred and deblurred radar images, generated from a numerical imaging model, the model exhibits exceptional performance, outperforming state-of- the-art methods across varying degrees and lengths of blur. Specifically, testing on 6,410 images yields mean squared error (MSE) and structural similarity index (SSIM) scores of 0.0086 and 0.9398, respectively. Additionally, validation on experimental measurements showcases promising results. This comprehensive evaluation underscores the effectiveness and versatility of the proposed approach, offering significant advancements in radar image processing for various applications such as target detection, recognition, surveillance, and navigation.