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Fast estimate of Hartley entropy in image sharpening
Author(s): Zuzana Krbcová; Jaromír Kukal; Jan Svihlik; Karel Fliegel
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Paper Abstract

Two classes of linear IIR filters: Laplacian of Gaussian (LoG) and Difference of Gaussians (DoG) are frequently used as high pass filters for contextual vision and edge detection. They are also used for image sharpening when linearly combined with the original image. Resulting sharpening filters are radially symmetric in spatial and frequency domains. Our approach is based on the radial approximation of unknown optimal filter, which is designed as a weighted sum of Gaussian filters with various radii. The novel filter is designed for MRI image enhancement where the image intensity represents anatomical structure plus additive noise. We prefer the gradient norm of Hartley entropy of whole image intensity as a measure which has to be maximized for the best sharpening. The entropy estimation procedure is as fast as FFT included in the filter but this estimate is a continuous function of enhanced image intensities. Physically motivated heuristic is used for optimum sharpening filter design by its parameter tuning. Our approach is compared with Wiener filter on MRI images.

Paper Details

Date Published: 28 September 2016
PDF: 6 pages
Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 99712I (28 September 2016); doi: 10.1117/12.2237743
Show Author Affiliations
Zuzana Krbcová, Univ. of Chemistry and Technology (Czech Republic)
Jaromír Kukal, Univ. of Chemistry and Technology (Czech Republic)
Jan Svihlik, Univ. of Chemistry and Technology (Czech Republic)
Czech Technical Univ. in Prague (Czech Republic)
Karel Fliegel, Czech Technical Univ. in Prague (Czech Republic)


Published in SPIE Proceedings Vol. 9971:
Applications of Digital Image Processing XXXIX
Andrew G. Tescher, Editor(s)

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