Abstract: The theory of Unified Focus and Defocus Analysis (UFDA) was presented by us earlier and it was extended to use both classical optimization technique and regularization approach for 3D scene recovery. In this paper we present a computational algorithm for UFDA which uses variable number of images in an optimal fashion. UFDA is based on modeling the sensing of defocused images in a camera system. This approach unifies Image Focus Analysis (IFA) and Image Defocus Analysis (IDA), which form two extremes in a range of possible methods useful in 3D shape and focused image recovery. The proposed computational algorithm consists of two main steps. In the first step, an initial solution is obtained by a combination of IFA, IDA, and interpolation. In the second step, the initial solution is refined by minimizing the error between the observed image data and the image data estimated using a given solution and the image formation model. A classical gradient descent or a regularization technique is used for error minimization. Our experiments indicate that the most difficult part of the algorithm is to obtain a reasonable solution for the focused image when only a few image frames are available. We employ several methods to address this part of the problem. The algorithm has been implemented and experimental results are presented.!18
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