DCT and PCA Based Method for Shape from Focus
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Depth Estimation by Finding Best Focused Points Using Line Fitting
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
3D Shape from Focus and Depth Map Computation Using Steerable Filters
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
3D shape recovery from image focus using kernel regression in eigenspace
Image and Vision Computing
Noise analysis for depth estimation
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Accurate 3D shape estimation based on combinatorial optimization
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Shape from focus using kernel regression
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A novel iterative shape from focus algorithm based on combinatorial optimization
Pattern Recognition
Shape from focus using fast discrete curvelet transform
Pattern Recognition
A Fuzzy-Neural approach for estimation of depth map using focus
Applied Soft Computing
Optimal depth estimation by combining focus measures using genetic programming
Information Sciences: an International Journal
Rectification of illumination in images used for shape from focus
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Analysis of focus measure operators for shape-from-focus
Pattern Recognition
3D shape from focus using LULU operators and discrete pulse transform in the presence of noise
Journal of Visual Communication and Image Representation
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The most popular shape from focus (SFF) methods in the literature are based on the concept of focused image surface (FIS)-the surface formed by the best focus points. According to paraxial-geometric optics, there is one-to-one correspondence between the shape of an object and the shape of its FIS. Therefore, the problem of three-dimensional (3-D) shape recovery from image focus can be described as the problem of determining the shape of the FIS. The conventional SFF method is inaccurate because of piecewise constant approximation of the FIS. The SFF method based on the FIS has shown better results by exhaustive search of the FIS shape using planar surface approximation at the cost of considerably higher computations. In this paper, search of the FIS shape is presented as an optimization problem, i.e., maximization of the focus measure in the 3-D image volume. The proposed method searches the optimal focus measure in the whole image volume, instead of the small volume as adopted in previous methods. The dynamic programming, instead of the approximation techniques, is used to search the optimal FIS shape. A direct application of dynamic programming on a 3-D data is impractical, because of higher computational complexity. Therefore a fast heuristic model based on dynamic programming is proposed for the search of FIS shape. The shape recovery results of the new method are better than previous methods. The proposed algorithm is significantly faster than the FIS algorithm, but a little slower than the conventional algorithm.