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Meeting and Seminar Archive:

Date:  June 2, 2008

Title: Enhancing Image Fidelity through Spatio-Spectral Design for Color Image Acquisition, Reconstruction, and Display

Speaker: Keigo Hirakawa, Postdoctoral Research Associate, Harvard University, Department of Statistics

 

Abstract: In the first part of the talk, we consider extending an image denoising problem to the problem of missing or incomplete pixel values---either due to mechanical designs or distortions. In the context of wavelet-based image processing, missing or incomplete pixels pose a particularly difficult challenge because none of the wavelet coefficients can be observed. In this talk, a unified framework for coupling the EM algorithm with the Bayesian hierarchical modeling of transform coefficients is presented. This empirical-Bayes strategy offers a statistically principled and extremely flexible approach to a wide range of pixel estimation problems including image denoising, image interpolation, super resolution, demosaicking.

In the second part of the talk, we consider the "throughput" of color imaging systems. Pixel values are typically sensed or displayed via a spatial subsampling procedure implemented as a color filter array---a physical construction whereby only a single color value is measured or displayed at each pixel location. Owing to the growing ubiquity of acquisition and display devices, much of recent work has focused on the implications of such arrays for subsequent digital processing, including in particular the canonical demosaicking task of reconstructing a full color image from spatially subsampled and incomplete color data acquired under a particular choice of array pattern. In contrast to the majority of the acquisition and display literature, we consider here the problem of color filter array design and its implications for spatial reconstruction quality. We prove the sub-optimality of a wide class of existing array patterns, and provide a constructive method for its solution that yields robust, new panchromatic designs implementable as subtractive colors.

 

Biography:

Keigo Hirakawa received the B.S.E. degree in electrical engineering from Princeton University, Princeton, NJ, in 2000, the M.S. and Ph.D. degrees in electrical and computer engineering from Cornell University, Ithaca, NY, in 2003 and 2005, respectively, and the M.M. degree in Jazz Performance from the New England Conservatory of Music, Boston, MA, in 2006. He is currently with the School of Engineering and Applied Sciences and the Department of Statistics at Harvard University, where he co-leads a collaboration with Sony Electronics, Inc. He has previously been an ASIC engineer and principal image scientist for the camera division of Hewlett-Packard/Agilent Technologies, and his past and current collaborations with camera manufacturers include Sony, Micron, Texas Instruments, and NEC. Dr. Hirakawa has received a Lockheed Martin fellowship award (2001) and a DoCoMo innovative paper award (jointly with Prof. Wolfe; IEEE ICIP 2007); he has also been selected a closing keynote speaker at IS&T CGIV 2008. His research focuses on statistical signal processing, color imaging, and computer vision.

Further information on the talk provided by Keigo Hirakawa:




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