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The anti-reflective transform and regularization by filtering. (English) Zbl 1258.94014

Van Dooren, Paul (ed.) et al., Numerical linear algebra in signals, systems and control. Selected papers based on the presentations at the international workshop, Kharagpur, India, January 9–11, 2007. In honor of Prof. Biswa Nath Datta. New York, NY: Springer (ISBN 978-94-007-0601-9/hbk; 978-94-007-0602-6/ebook). Lecture Notes in Electrical Engineering 80, 1-21 (2011).
Summary: Filtering methods are used in signal and image restoration to reconstruct an approximation of a signal or image from degraded measurements. Filtering methods rely on computing a singular value decomposition or a spectral factorization of a large structured matrix. The structure of the matrix depends in part on imposed boundary conditions. Anti-reflective boundary conditions preserve continuity of the image and its (normal) derivative at the boundary, and have been shown to produce superior reconstructions compared to other commonly used boundary conditions, such as periodic, zero and reflective. The purpose of this paper is to analyze the eigenvector structure of matrices that enforce anti-reflective boundary conditions. In particular, a new anti-reflective transform is introduced, and an efficient approach to computing filtered solutions is proposed. Numerical tests illustrate the performance of the discussed methods.
For the entire collection see [Zbl 1220.65005].

MSC:

94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
94A12 Signal theory (characterization, reconstruction, filtering, etc.)
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