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Recursive regularization filters: design, properties, and applications

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3 Author(s)
Unser, M. ; Nat. Inst. of Health, Bethesda, MD ; Aldroubi, A. ; Eden, M.

Least squares approximation problems that are regularized with specified highpass stabilizing kernels are discussed. For each problem, there is a family of discrete regularization filters (R-filters) which allow an efficient determination of the solutions. These operators are stable symmetric lowpass filters with an adjustable scale factor. Two decomposition theorems for the z-transform of such systems are presented. One facilitates the determination of their impulse response, while the other allows an efficient implementation through successive causal and anticausal recursive filtering. A case of special interest is the design of R-filters for the first- and second-order difference operators. These results are extended for two-dimensional signals and, for illustration purposes, are applied to the problem of edge detection. This leads to a very efficient implementation (8 multiplies+10 adds per pixel) of the optimal Canny edge detector based on the use of a separable second-order R-filter

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:13 ,  Issue: 3 )

Date of Publication:

Mar 1991

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