One- and two-dimensional least-squares smoothing and edge-sharpening method for image processing | Zendy

Mgh Bell | Zendy

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One- and two-dimensional least-squares smoothing and edge-sharpening method for image processing

Author(s) -

Mgh Bell

Publication year - 1976

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/4102888

Subject(s) - sharpening , smoothing , quartic function , mathematics , quadratic function , weighting , function (biology) , point (geometry) , convolution (computer science) , quadratic equation , least squares function approximation , image (mathematics) , algorithm , computer science , statistics , artificial intelligence , geometry , physics , evolutionary biology , estimator , acoustics , artificial neural network , pure mathematics , biology

A rapid method is developed for two-dimensional smoothing and edge- sharpening by the least-squares fitting of a function to a limited area of the data. This convolution or matrix weighting is applied at each point of the data set to yield a smoothed or a sharpened image. Weighting matrices for 3 x 3, 5 x 5, and 7 x 7 point fitting areas are provided for polynomial function fits of all degrees up to the highest degree determinable. For the 7 x 7 point fitting area weights for fitting functions of up to the quartic in both dimensions are supplied. Application of the 5 x 5 point quadratic fit smoothing to a nuclear medicine image is shown as an example. (auth

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