Depth from Defocus Estimation in Spatial Domain

Abstract

This paper presents an algorithm for a dense computation of the difference in blur between two images. The two images are acquired by varying the intrinsic parameters of the camera. The image formation system is assumed to be passive. Estimation of depth from the blur difference is straightforward. The algorithm is based on a local image decomposition technique using the Hermite polynomial basis. We show that any coefficient of the Hermite polynomial computed using the more blurred image is a function of the partial derivatives of the other image and the blur difference. Hence, the blur difference is computed by resolving a system of equations. The resulting estimation is dense and involves simple local operations carried out in the spatial domain. The mathematical developments underlying estimation of the blur in both 1D and 2D images are presented. The behavior of the algorithm is studied for constant images, step edges, line edges, and junctions. The selection of its parameters is discussed. The proposed algorithm is tested using synthetic and real images. The results obtained are accurate and dense. They are compared with those obtained using an existing algorithm.