Statistics of Stochastic Textures: Application in pattern analysis and image processing

Author

Zachevsky, Ido ; Zeevi, Yehoshua Y.

Author_Institution

Dept. of Electr. Eng., Technion - Israel Inst. of Technol., Haifa, Israel

fYear

2014

fDate

3-5 Dec. 2014

Firstpage

1

Lastpage

5

Abstract

Unlike the well-established fact of non-Gaussian, highly kurtotic, statistical property that characterizes the general class of natural images, a wide class of Natural Stochastic Textures (NST) obeys, to a good approximation, Gaussianity. This is exploited in denoising, resolution enhancement, analysis, modelling and classification of textures and textured images. Denoising is performed by decomposition to cartoon and textural layers. A fractal model is used to restore the latter. Deconvolution is performed via a variational scheme in the frequency domain, using phase and long-range dependency properties of NST.

Keywords

frequency-domain analysis; image classification; image denoising; image enhancement; image resolution; image texture; stochastic processes; NST; fractal model; frequency domain; image denoising; image processing; natural stochastic textures; pattern analysis; resolution enhancement; textural layers; texture classification; textured images; variational scheme; Fractals; Gaussian distribution; Image restoration; Noise; Noise measurement; Noise reduction; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical & Electronics Engineers in Israel (IEEEI), 2014 IEEE 28th Convention of

Conference_Location

Eilat

Print_ISBN

978-1-4799-5987-7

Type

conf

DOI

10.1109/EEEI.2014.7005899

Filename

7005899