Many methods for deconvolving images assume either that the entire convolution is available, or that the convolution is adequately modelled as a circular convolution. In reality, neither is usually the case, and only a section of a much larger blurred (and contaminated) image is observed. The truncation gives rise to null objects in reconstructions obtained by deconvolution methods. It is possible to formulate the problem as exact, though underdetermined, and to apply singular value decomposition to deriving an inverse operator. We compare different practical methods for performing deconvolution with a scanning finite impulse response filter derived in this manner.

Date Published: 9 December 1997
PDF: 12 pages
Proc. SPIE 3171, Computational, Experimental, and Numerical Methods for Solving Ill-Posed Inverse Imaging Problems: Medical and Nonmedical Applications, (9 December 1997); doi: 10.1117/12.284711

Published in SPIE Proceedings Vol. 3171:
Computational, Experimental, and Numerical Methods for Solving Ill-Posed Inverse Imaging Problems: Medical and Nonmedical Applications
Randall Locke Barbour; Mark J. Carvlin; Michael A. Fiddy, Editor(s)