SPHR-SAR-Net：基于非局部全变分的超像素高分辨率SAR成像网络
SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation
AI总结：
针对传统高分辨率SAR成像忽略非局部信息、散斑难滤除及计算复杂度高的问题，提出SPHR-SAR-Net，通过结合非凸非局部全变分复合正则化并展开为深度展开网络，实现快速且准确的高分辨率SAR成像。
摘要：
高分辨率是合成孔径雷达（SAR）发展的关键趋势，它能够捕获精细细节并准确表示后向散射特性。然而，传统的高分辨率SAR成像算法面临几个挑战。首先，这些算法往往关注局部信息，忽略了不同像素块之间的非局部信息。其次，在高分辨率SAR图像中，散斑更加明显且难以滤除。第三，高分辨率SAR成像过程通常涉及较高的时间和计算复杂度，使得实时成像难以实现。为了解决这些问题，我们提出了一种超像素高分辨率SAR成像网络（SPHR-SAR-Net），用于高分辨率SAR模式下的快速去散斑。基于超像素技术的概念，我们最初将非凸和非局部全变分结合作为复合正则化。这种方法在减少凸约束引起的偏差效应的同时，更有效地进行去散斑并管理像素之间的关系。随后，我们使用交替方向乘子法（ADMM）算法求解复合正则化模型，并将其展开为深度展开网络（DUN）。网络的参数以数据驱动的方式自适应学习，学习到的网络显著提高了成像速度。此外，深度展开网络兼容聚束、凝视聚束和滑动聚束等高分辨率成像模式。在本文中，我们通过模拟和真实SAR场景的实验证明了SPHR-SAR-Net的优越性。结果表明，SPHR-SAR-Net能够从原始回波数据中快速执行高分辨率SAR成像，产生准确的成像结果。
英文摘要：
High-resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting non-local information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a Superpixel High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in high-resolution SAR mode. Based on the concept of superpixel techniques, we initially combine non-convex and non-local total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the Deep Unfolded Network is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results.
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