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UBC Theses and Dissertations

Iterative depth from defocus (I-DFD) algorithms Sewlochan, Ray


Depth From Defocus (DFD) seeks to obtain depth information from camera images by measuring the amount of defocus in the images. Although DFD avoids many of the problems associated with stereo vision by maintaining only one point of view, other issues such as optical modelling and computational intensity determine speed and accuracy of DFD systems. This thesis presents the optical theory that establishes the relationship between defocus and depth. Defocus is modelled by the convolution of a scene and a defocus kernel to produce a defocussed image. Dependencies on the scene are removed by measuring the relative defocus between two images of the scene. This is modeled by the convolution of the sharper image with a relative defocus kernel to produce the blurrier image. The determination of the relative defocus kernel is the challenge of DFD. Spatial domain algorithms avoid the errors and assumptions associated with frequency domain transformations. Iterative techniques provide an accurate way of finding the relative defocus kernel, at the expense of computational intensity required by convolution calculations. This thesis develops a hierarchy of Iterative DFD (I-DFD) algorithms for the spatial domain. The hierarchy is divided into defocus modelling method (theoretical (T) or measured (M)), relative defocus shaping method (Gaussian (G) or regularized (R)) and convolution implementation (two-dimensional (2), separable (S) or integrated (I)). Four of the algorithms (TR2, TRI, TGS, TGI) in this hierarchy are implemented and fully tested on three classes of images. TR2 has been previously published and is used as a benchmark. TGS and TGI significantly reduce computational effort, but suffer from degraded accuracy. TRI reduces computational effort by several orders of magnitude with no degradation of accuracy for two of the three classes of images. Recommendations are made to alleviate the degradation in the third class of images.

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