Optimized aperture shapes for depth estimation

Sellent, Anita; Favaro, Paolo (2014). Optimized aperture shapes for depth estimation. Pattern recognition letters, 40, pp. 96-103. Elsevier 10.1016/j.patrec.2013.12.019

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The finite depth of field of a real camera can be used to estimate the depth structure of a scene. The distance of an object from the plane in focus determines the defocus blur size. The shape of the blur depends on the shape of the aperture. The blur shape can be designed by masking the main lens aperture. In fact, aperture shapes different from the standard circular aperture give improved accuracy of depth estimation from defocus blur. We introduce an intuitive criterion to design aperture patterns for depth from defocus. The criterion is independent of a specific depth estimation algorithm. We formulate our design criterion by imposing constraints directly in the data domain and optimize the amount of depth information carried by blurred images. Our criterion is a quadratic function of the aperture transmission values. As such, it can be numerically evaluated to estimate optimized aperture patterns quickly. The proposed mask optimization procedure is applicable to different depth estimation scenarios. We use it for depth estimation from two images with different focus settings, for depth estimation from two images with different aperture shapes as well as for depth estimation from a single coded aperture image. In this work we show masks obtained with this new evaluation criterion and test their depth discrimination capability using a state-of-the-art depth estimation algorithm.

Item Type:

Journal Article (Original Article)


08 Faculty of Science > Institute of Computer Science (INF) > Computer Vision Group (CVG)
08 Faculty of Science > Institute of Computer Science (INF)

UniBE Contributor:

Sellent, Anita and Favaro, Paolo


000 Computer science, knowledge & systems
500 Science > 510 Mathematics








Paolo Favaro

Date Deposited:

27 Apr 2015 15:47

Last Modified:

16 Aug 2017 09:19

Publisher DOI:






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