Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey.
IEEE Trans Image Process. 2012 Apr;21(4):2075-88. doi: 10.1109/TIP.2011.2179056. Epub 2011 Dec 9.
Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data, which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm, where each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements that it provides over existing techniques for model error compensation in SAR.
在各种应用中，图像形成算法都对观测过程的数学模型有明确或隐含的依赖。观测模型的不准确性可能会导致重建图像出现各种退化和伪像。本文关注的应用是合成孔径雷达（SAR）成像，它特别容易受到运动引起的模型误差的影响。这些类型的误差会导致 SAR 数据中的相位误差，从而导致重建图像的散焦。特别关注能够稀疏表示的场的成像，我们提出了一种基于稀疏性的 SAR 成像和相位误差校正联合方法。相位误差校正在图像形成过程中进行。该问题在基于非二次正则化的框架中被设置为优化问题。该方法涉及一种迭代算法，其中每个迭代都由图像形成和模型误差校正的连续步骤组成。实验结果表明，该方法对于各种类型的相位误差都非常有效，并且在 SAR 中的模型误差补偿方面，它提供了比现有技术更好的改进。
