Shape from focus using fast discrete curvelet transform

Authors:
Rashid Minhas;Abdul Adeel Mohammed;Q. M. Jonathan Wu
Affiliations:
Department of Electrical and Computer Engineering, University of Windsor, Ontario, Canada N9B 3P4;Department of Electrical and Computer Engineering, University of Windsor, Ontario, Canada N9B 3P4;Department of Electrical and Computer Engineering, University of Windsor, Ontario, Canada N9B 3P4
Venue:
Pattern Recognition
Year:
2011

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Abstract

A new method for focus measure computation is proposed to reconstruct 3D shape using image sequence acquired under varying focus plane. Adaptive histogram equalization is applied to enhance varying contrast across different image regions for better detection of sharp intensity variations. Fast discrete curvelet transform (FDCT) is employed for enhanced representation of singularities along curves in an input image followed by noise removal using bivariate shrinkage scheme based on locally estimated variance. The FDCT coefficients with high activity are exploited to detect high frequency variations of pixel intensities in a sequence of images. Finally, focus measure is computed utilizing neighborhood support of these coefficients to reconstruct the shape and a well-focused image of the scene being probed.