Performance Analysis of Maximum Likelihood Estimator for Recovery of Depth from Defocused Images and Optimal Selection ofCamera Parameters

  • Authors:

  • A. N. Rajagopalan;S. Chaudhuri

  • Affiliations:

  • Department of Electrical Engineering, Indian Institute of Technology, Bombay, 400076, India. E-mail: raju@ee.iitb.ernet.in;Department of Electrical Engineering, Indian Institute of Technology, Bombay, 400076, India. E-mail: sc@ee.iitb.ernet.in

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 1998

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Abstract

The recovery of depth from defocused images involves calculating the depthof various points in a scene by modeling the effect that the focal parametersof the camera have on images acquired with a small depth of field.In the existing methods on depth from defocus (DFD), two defocusedimages of a scene are obtained by capturing the scene withdifferent sets of camera parameters.Although the DFD technique is computationally simple, the accuracy is somewhatlimited compared to the stereo algorithms. Further,an arbitrary selection of the camera settings can result in observed imageswhose relative blurring is insufficient to yield a good estimate of the depth.In this paper, we address the DFD problem as a maximum likelihood (ML)based blur identificationproblem. We carry out performance analysis of the ML estimatorand study the effect of the degree of relativeblurring on the accuracy of the estimate of the depth.We propose a criterion for optimal selection of camera parameters to obtainan improved estimate of the depth. The optimality criterion is based on theCramer-Rao bound of the variance of the error in the estimate of blur.A number of simulations as well as experimental results on real imagesare presented to substantiate our claims.