In the depth from defocus (DFD) method, two defocused images of a scene are obtained by capturing the scene with different sets of camera parameters. An arbitrary selection of the camera settings can result in observed images whose relative blurring is insufficient to yield a good estimate of the depth. In this paper, we study the effect of the degree of relative blurring on the accuracy of the estimate of the depth by addressing the DFD problem in a maximum likelihood-based framework. We

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... e a criterion for optimal selection of camera parameters to obtain an improved estimate of the depth. The optimality criterion is based on the Cramer-Rao bound of the variance of the error in the estimate of blur. Simulations as well as experimental results on real images are presented for validation.