Depth from Defocus via Discriminative Metric Learning

Abstract

In this paper, we propose a discriminative learning-based method for recovering the depth of a scene from multiple defocused images. The proposed method consists of a discriminative learning phase and a depth estimation phase. In the discriminative learning phase, we formalize depth from defocus (DFD) as a multi-class classification problem which can be solved by learning the discriminative metrics from the synthetic training set by minimizing a criterion function. To enhance the discriminative and generalization performance of the learned metrics, the criterion takes both within-class and between-class variations into account, and incorporates margin constraints. In the depth estimation phase, for each pixel, we compute the N discriminative functions and determine the depth level according to the minimum discriminant value. Experimental results on synthetic and real images show the effectiveness of our method in providing a reliable estimation of the depth of a scene.