Space-Variant Approaches to Recovery of Depth from Defocused Images

Abstract

The recovery of depth from defocused images involves calculating the depth of various points in a scene by modeling the effect that the focal parameters of the camera have on images acquired with a small depth of field. In the approach to depth from defocus (DFD), previous methods assume the depth to be constant over fairly large local regions and estimate the depth through inverse filtering by considering the system to be shift-invariant over those local regions. But a subimage when analyzed in isolation introduces errors in the estimate of the depth. In this paper, we propose two new approaches for estimating the depth from defocused images. The first approach proposed here models the DFD system as a block shift-variant one and incorporates the interaction of blur among neighboring subimages in an attempt to improve the estimate of the depth. The second approach looks at the depth from defocus problem in the space-frequency representation framework. In particular, the complex spectrogram and the Wigner distribution are shown to be likely candidates for recovering the depth from defocused images. The performances of the proposed methods are tested on both synthetic and real images. The proposed methods yield good results and the quality of the estimates obtained using these methods is compared with the existing method.