CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2005 vol.27 Issue No.03 - March
Issue No.03 - March (2005 vol.27)
Paolo Favaro , IEEE
Stefano Soatto , IEEE
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.43
We introduce a novel approach to shape from defocus, i.e., the problem of inferring the three-dimensional (3D) geometry of a scene from a collection of defocused images. Typically, in shape from defocus, the task of extracting geometry also requires deblurring the given images. A common approach to bypass this task relies on approximating the scene locally by a plane parallel to the image (the so-called equifocal assumption). We show that this approximation is indeed not necessary, as one can estimate 3D geometry while avoiding deblurring without strong assumptions on the scene. Solving the problem of shape from defocus requires modeling how light interacts with the optics before reaching the imaging surface. This interaction is described by the so-called point spread function (PSF). When the form of the PSF is known, we propose an optimal method to infer 3D geometry from defocused images that involves computing orthogonal operators which are regularized via functional singular value decomposition. When the form of the PSF is unknown, we propose a simple and efficient method that first learns a set of projection operators from blurred images and then uses these operators to estimate the 3D geometry of the scene from novel blurred images. Our experiments on both real and synthetic images show that the performance of the algorithm is relatively insensitive to the form of the PSF. Our general approach is to minimize the Euclidean norm of the difference between the estimated images and the observed images. The method is geometric in that we reduce the minimization to performing projections onto linear subspaces, by using inner product structures on both infinite and finite-dimensional Hilbert spaces. Both proposed algorithms involve only simple matrix-vector multiplications which can be implemented in real-time.
Shape from defocus, depth from defocus, blind deconvolution, image processing, deblurring, shape, 3D reconstruction, shape estimation, image restoration, learning subspaces.
Paolo Favaro, Stefano Soatto, "A Geometric Approach to Shape from Defocus", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.27, no. 3, pp. 406-417, March 2005, doi:10.1109/TPAMI.2005.43