Curvelet transform based image denoising via Gaussian mixture model

Author

Engin, M. Alptekin ; Cavusoglu, Bulent

Author_Institution

Elektrik Elektron. Muhendisligi Bolumu, Ataturk Univ., Erzurum, Turkey

fYear

2014

fDate

23-25 April 2014

Firstpage

1499

Lastpage

1502

Abstract

This paper presents a novel image denoising method based on curvelet transform and gaussian mixture model. After decomposing noisy images into curvelet domain, gaussian mixture model (GMM) is applied and obtained statistical parameters are used for calculating adaptive level depended thresholds. Noise removal is performed using hard threshold method in the curvelet coefficients of each sub-band. Due to the adaptive thresholding for each level the restored images are visually satisfactory.

Keywords

Gaussian processes; curvelet transforms; image denoising; image restoration; image segmentation; mixture models; statistical analysis; GMM; Gaussian mixture model; adaptive level depended threshold calculation; adaptive thresholding; curvelet transform; hard threshold method; image denoising method; image restoration; noise removal; noisy image decomposition; statistical parameter; Conferences; Gaussian mixture model; Image denoising; Image restoration; Signal processing; Transforms; Curvelet transform; Gaussian mixture model; denoising;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2014 22nd

Conference_Location

Trabzon

Type

conf

DOI

10.1109/SIU.2014.6830525

Filename

6830525