Joint Reconstruction-Segmentation on Graphs

Practical image segmentation tasks concern images which must be reconstructed from noisy, distorted, and/or incomplete observations. A recent approach for solving such tasks is to perform this reconstruction jointly with the segmentation, using each to guide the other. However, this work has so far employed relatively simple segmentation methods, such as the Chan--Vese algorithm. In this paper, we present a method for joint reconstruction-segmentation using graph-based segmentation methods, which have been seeing increasing recent interest. Complications arise due to the large size of the matrices involved, and we show how these complications can be managed. We then analyze the convergence properties of our scheme. Finally, we apply this scheme to distorted versions of ``two cows images familiar from previous graph-based segmentation literature, first to a highly noised version and second to a blurred version, achieving highly accurate segmentations in both cases. We compare these results to those obtained by sequential reconstruction-segmentation approaches, finding that our method competes with, or even outperforms, those approaches in terms of reconstruction and

Automated summary

This paper presents a method for jointly reconstructing and segmenting images using graph-based segmentation methods. The convergence properties of the scheme are analyzed, and the performance in image reconstruction is evaluated through experiments on distorted images. The results show that the proposed method achieves highly accurate segmentations and outperforms sequential reconstruction-segmentation approaches in terms of reconstruction and segmentation.