Thin structures retrieval using anisotropic neighborhoods of superpixels: application to shape-from-focus

Abstract

Shape-From-Focus (SFF) refers to the challenging inverse problem of recovering the scene depth from a given set of focused images using a static camera. Standard approaches model the interactions between neighboring pixels to get a regularized solution. Nevertheless, isotropic regularization is known to introduce undesired artifacts and to remove early thin structures. These structures have a small size in at least one dimension and are more numerous when considering superpixel preprocessing. This paper addresses the improvement of SFF regularization through the estimation of the presence of such structures and the construction of anisotropic neighborhoods sticking along image edges and proposes a flexible formulation over pixels or superpixels. A thoroughly study comparing different strategies for constructing these neighborhoods in terms of accuracy and running time for the targeted application is provided. Notably, experiments performed on a reference dataset show the overall superiority of the approach, e.g. a decrease of the RMSE value by about 20%, and its robustness against generated superpixels.