Army Engineering University, Shijiazhuang, China.
PLoS One. 2023 Feb 10;18(2):e0276051. doi: 10.1371/journal.pone.0276051. eCollection 2023.
Current autofocus algorithms utilizing image criteria impose a significant computational burden. Therefore, this paper proposes a computationally efficient autofocus algorithm combined with SAR image feature points, employing the Prewitt operator to obtain the SAR image features. The range cell with the number of feature points in the front row as the input of the autofocus method to perform motion error estimation and compensation on SAR imagery. Our method's key feature is to optimize the selection criteria of range cells by acquiring the feature points of SAR images,reduces the number of input range cell,reduce the computational complexity of the autofocus algorithm and ultimately enhance the focusing effect of SAR images. Trials involving simulation and measured data demonstrate the effectiveness of the developed method.
当前利用图像准则的自动对焦算法会带来很大的计算负担。因此，本文提出了一种与 SAR 图像特征点相结合的计算效率高的自动对焦算法，使用 Prewitt 算子获取 SAR 图像特征。以特征点数最多的一行的距离单元作为自动对焦方法的输入，对 SAR 图像进行运动误差估计和补偿。我们的方法的关键特点是通过获取 SAR 图像的特征点来优化距离单元的选择标准，减少输入距离单元的数量，降低自动对焦算法的计算复杂度，最终增强 SAR 图像的聚焦效果。涉及仿真和实测数据的试验证明了所提出方法的有效性。
