Communication and Information Theory, Berlin Institute of Technology, 10623 Berlin, Germany.
Industry Grade Networks and Clouds, Faculty of Electrical Engineering, and Computer Science, Berlin Institute of Technology, 10623 Berlin, Germany.
Sensors (Basel). 2022 Jul 25;22(15):5533. doi: 10.3390/s22155533.
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.
块稀疏正则化在主动热成像中已经得到了很好的应用，并被用于基于多次测量的逆问题。该方法的主要瓶颈是正则化参数的选择，这因每个实验而异。我们展示了使用学习的块迭代收缩阈值算法（LBISTA）的好处，该算法能够学习正则化参数的选择，而无需手动选择。此外，LBISTA 能够确定合适的权矩阵来解决底层的逆问题。因此，在本文中，我们提出了 LBISTA，并将其与基于合成和主动热成像实验测试数据的最新块迭代收缩阈值算法进行了比较，以实现缺陷重建。我们的结果表明，使用学习的块稀疏优化方法在固定的少量迭代次数下可以提供更小的归一化均方误差。因此，这使得我们能够提高收敛速度，仅需要几次迭代就能在光热超分辨率成像中生成准确的缺陷重建。
