Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575271
Title: Coded aperture imaging
Author: Martinello, Manuel
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2012
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
This thesis studies the coded aperture camera, a device consisting of a conventional camera with a modified aperture mask, that enables the recovery of both depth map and all-in-focus image from a single 2D input image. Key contributions of this work are the modeling of the statistics of natural images and the design of efficient blur identification methods in a Bayesian framework. Two cases are distinguished: 1) when the aperture can be decomposed in a small set of identical holes, and 2) when the aperture has a more general configuration. In the first case, the formulation of the problem incorporates priors about the statistical variation of the texture to avoid ambiguities in the solution. This allows to bypass the recovery of the sharp image and concentrate only on estimating depth. In the second case, the depth reconstruction is addressed via convolutions with a bank of linear filters. Key advantages over competing methods are the higher numerical stability and the ability to deal with large blur. The all-in-focus image can then be recovered by using a deconvolution step with the estimated depth map. Furthermore, for the purpose of depth estimation alone, the proposed algorithm does not require information about the mask in use. The comparison with existing algorithms in the literature shows that the proposed methods achieve state-of-the-art performance. This solution is also extended for the first time to images affected by both defocus and motion blur and, finally, to video sequences with moving and deformable objects.
Supervisor: Favaro, Paolo Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.575271  DOI: Not available
Share: