Discrete Iterative Partial Segmentation Technique (DIPS) for Tomographic Reconstruction

Discrete tomography refers to tomographic reconstruction of images that are known to contain only a few intensity levels. We propose a new reconstruction technique for discrete tomography that uses a relaxed partial segmentation and a refinement update in each iteration. With our approach the dimension of the tomographic reconstruction problem is carefully and slowly reduced as the image structures become more evident, allowing us to capture object details more accurately. The method is complemented with an appropriate sparsity model to incorporate the prior knowledge of the nature of the data and further improve the reconstruction. As a proof of concept, we compare our method with the current standard and state-of-the art techniques and in simulation experiments show substantial improvement in the accuracy of the reconstruction in all of the simulation cases considered as well as moderate improvement on experimental data.