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Proceedings Paper

Cramer-Rao lower bounds for support-constrained and pixel-based multiframe blind deconvolution
Author(s): Charles L. Matson; Alim Haji
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Paper Abstract

Multi-frame blind deconvolution (MFBD) algorithms can be used to reconstruct a single high-resolution image of an object from one or more measurement frames of that are blurred and noisy realizations of that object. The blind nature of MFBD algorithms permits the reconstruction process to proceed without having separate measurements or knowledge of the blurring functions in each of the measurement frames. This is accomplished by estimating the object common to all the measurement frames jointly with the blurring functions that are different from frame to frame. An issue of key importance is understanding how accurately the object pixel intensities can be estimated with the use of MFBD algorithms. Here we present algorithm-independent lower bounds to the variances of estimates of the object pixel intensities to quantify the accuracy of these estimates when the blurring functions are estimated pixel by pixel. We employ support constraints on both the object and the blurring functions to aid in making the inverse problem unique. The lower bounds are presented as a function of the sizes and shapes of these support regions and the number of measurement frames.

Paper Details

Date Published: 29 September 2006
PDF: 10 pages
Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 636503 (29 September 2006); doi: 10.1117/12.688405
Show Author Affiliations
Charles L. Matson, US Air Force Research Lab. (United States)
Alim Haji, US Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 6365:
Image and Signal Processing for Remote Sensing XII
Lorenzo Bruzzone, Editor(s)

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