An MRF Model-Based Approach to Simultaneous Recovery of Depth and Restoration from Defocused Images

ÐDepth from defocus (DFD) problem 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 this paper, we propose a MAP-MRF-based scheme for recovering the depth and the focused image of a scene from two defocused images. The space-variant blur parameter and the focused image of the scene are both modeled as MRFs and their MAP estimates are obtained using simulated annealing. The scheme is amenable to the incorporation of smoothness constraints on the spatial variations of the blur parameter as well as the scene intensity. It also allows for inclusion of line fields to preserve discontinuities. The performance of the proposed scheme is tested on synthetic as well as real data and the estimates of the depth are found to be better than that of the existing window-based DFD technique. The quality of the space-variant restored image of the scene is quite good even under severe space-varying blurring conditions. Index TermsÐDepth from defocus, space-variant blur, space-variant image restoration, Markov random field, smoothness constraint, line fields, Gibbs distribution, maximum a posteriori, simulated annealing.