Synthetic aperture radar images denoising based on multi-scale attention cascade convolutional neural network
Huilin Shan, Xiangwei Fu, Zongkui Lv, Xingchen Xu, Xingtao Wang, Yinsheng Zhang
Abstract
Abstract Synthetic aperture radar (SAR) images are often affected by speckle noise, which can hinder accurate interpretation and subsequent use of the images in applications such as target detection and segmentation. To address this issue, we propose a denoising algorithm based on a multi-scale attention cascade convolutional neural network (MSAC-Net). Our algorithm employs multi-scale asymmetric convolution to extract image features and an attention mechanism to integrate these features. Additionally, we designed a multi-layer deep cascade convolutional network to enhance the generalization ability of the model features. Experimental results show that our proposed MSAD-Net model significantly outperforms state-of-the-art SAR image denoising algorithms. Specifically, it achieves a significant improvement in peak signal-to-noise ratio, with an increase of about 0.81–13.97 dB, and structural similarity index measure, with an increase of about 0.01–0.14. Overall, our study presents a novel denoising algorithm for SAR images that greatly improves the accuracy of subsequent image applications.