Depth Hypotheses Fusion through 3D Weighted Least Squares in Shape from Focus.

Abstract

In shape-from-focus (SFF) methods, a single focus measure is used to compute the focus volume. However, it seems that a single focus measure operator is not capable of computing accurate focus values for the images of diverse types of object shapes. Furthermore, most of the SFF methods try to improve the depth map without considering any additional structural or prior information. Consequently, the extracted shape of the object might lack important details. In this work, we address these problems and suggest a method in which depth hypotheses are combined for a more accurate 3D shape through 3D weighted least squares. First, depth hypotheses are obtained by applying a number of focus operators. Then, structural prior or guidance volume is extracted from the focus measure volumes. Finally, a 3D weighted least squares optimization technique is applied to the depth hypothesis volume, where weights are computed from the guidance volume. Thus, by inducing structural prior, an improved resultant depth map is obtained. The proposed method was tested using various image sequences of synthetic and microscopic real objects. Experimental results and comparative analysis demonstrated the effectiveness of the proposed method.