Many image processing applications require computing approximate solutions of very large, ill-conditioned linear systems. Physical assumptions of the imaging system usually mean that the matrices in these linear systems have exploitable structure. The specific structure depends on (usually simplifying) assumptions of the physical model, and other considerations such as boundary conditions, but one often hears about circulant, Toeplitz and Hankel matrices in these applications. Kronecker products also occur, but this structure is not obvious from measured data. In this talk we discuss a scheme for computing a (possibly approximate) Kronecker product decomposition of structured matrices in image processing, which extends previous work by Kamm and Nagy to a wider class of problems.
1 Joint work with Michael NG and Lisa PERRONE.