Image denoising in hybrid wavelet and quincunx diamond filter bank domain based on Gaussian scale mixture model. (August 2015)

• Record Type: Journal Article
• Authors: Amala Shanthi, S.; Sulochana, C. Helen; Latha, T.
• Is Part Of: Computers & electrical engineering. Volume 46(2015)
• Page Start: 384
• Page End: 393
• Publication Date: 2015-08
• Subjects: Denoising algorithms -- Half-band filter -- Wavelet filter bank -- Quincunx diamond filter bank -- Gaussian scale mixture model; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.02.002
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: We propose a new denoising algorithm using wavelet and quincunx diamond filter bank. The subband coefficients are modeled with Gaussian scale mixture model. Bayes least means square is used to obtain the denoised coefficients. The new method does spatial averaging without smoothing the edges. Abstract: The main challenge in image denoising is, how to preserve the information such as edges and textures to get satisfactory visual quality when improving the signal to noise ratio. In this paper, we propose a hybrid filter bank for denoising based on wavelet filter bank and quincunx diamond filter bank. The noisy image is decomposed into different subbands of frequency and orientation using DMeyer wavelet. The quincunx diamond filter bank is designed from finite impulse response (FIR) filters using Kaiser window, which is applied on the detail subband of wavelet filter bank. The directional subband coefficients are modeled with Gaussian scale mixture model (GSM). The Bayes least squares estimator is used to obtain the denoised detail coefficients from the noisy image decomposition. Experimental results show that the new method performs spatial averaging without smoothing edges, and thereby enhances the visual quality and peak signal-to-noise-ratio.