Divide and Conquer: Using Spectral Methods on Partitioned Images
Dianne P. O'Leary, Julianne Chung, and Glenn Easley - University of Maryland, College Park
Abstract
Spectral filtering methods such as Tikhonov regularization
are powerful tools in deblurring images, but their usefulness
is limited by two factors: the expense of the computation
and the lack of flexibility in the filter function.
This talk focuses on overcoming these limitations by
partitioning the data in order to better process each
segment. The data division can take several forms:
for example, partitioning by spectral frequency, by
input channel, or by subimages.
Benefits of a divide and conquer approach to imaging can
include smaller error, better conditioning of
subproblems, and faster computations. We give examples
using various data partitionings.
This talk is organized by Howard Elman