Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging
This work provides an incremental improvement for researchers and practitioners in active thermal imaging by automating the selection of regularization parameters, which is a known bottleneck.
This paper addresses the challenge of manually selecting regularization parameters in block-sparse regularization for active thermal imaging. By proposing a learned block iterative shrinkage thresholding algorithm, the authors demonstrate that their approach achieves smaller normalized mean square errors and improved convergence speed for defect reconstruction in photothermal super-resolution imaging, requiring fewer iterations than traditional methods.
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. To avoid time-consuming manually selected regularization parameter, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data 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 than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging.