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In recent years, the usefulness of 3D shape estimation is being realized inmicroscopic or close-range imaging, as the 3D information can further be usedin various applications. Due to limited depth of field at such small distances,the defocus blur induced in images can provide information about the 3D shapeof the object. The task of `shape from defocus' (SFD), involves the problem ofestimating good quality 3D shape estimates from images with depth-dependentdefocus blur. While the research area of SFD is quite well-established, theapproaches have largely demonstrated results on objects with bulk/coarse shapevariation. However, in many cases, objects studied under microscopes ofteninvolve fine/detailed structures, which have not been explicitly considered inmost methods. In addition, given that, in recent years, large data volumes aretypically associated with microscopy related applications, it is also importantfor such SFD methods to be efficient. In this work, we provide an indication ofthe usefulness of the Belief Propagation (BP) approach in addressing theseconcerns for SFD. BP has been known to be an efficient combinatorialoptimization approach, and has been empirically demonstrated to yield goodquality solutions in low-level vision problems such as image restoration,stereo disparity estimation etc. For exploiting the efficiency of BP in SFD, weassume local space-invariance of the defocus blur, which enables theapplication of BP in a straightforward manner. Even with such an assumption,the ability of BP to provide good quality solutions while using non-convexpriors, reflects in yielding plausible shape estimates in presence of finestructures on the objects under microscopy imaging.

Shape Estimation from Defocus Cue for Microscopy Images via Belief Propagation