Translator Disclaimer
29 September 2006 Cramer-Rao lower bounds for support-constrained and pixel-based multiframe blind deconvolution
Author Affiliations +
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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Charles L. Matson and Alim Haji "Cramer-Rao lower bounds for support-constrained and pixel-based multiframe blind deconvolution", Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 636503 (29 September 2006);


High contrast imaging in the presence of turbulence
Proceedings of SPIE (May 05 2012)
Simulation the optical part of an image capture system
Proceedings of SPIE (January 07 2009)
Superresolution of bar codes
Proceedings of SPIE (October 06 1998)
Three-dimensional image capture by volume imaging
Proceedings of SPIE (January 01 1990)
The impact of low signal to noise ratio values on...
Proceedings of SPIE (October 11 2010)

Back to Top