DocumentCode :
152766
Title :
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
Link To Document :
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