Abstract
In recent years, a great deal of effort has been expended on developing robust solutions for images quality degradation caused mainly by noise. In this paper, we explore this area of research and we propose a new unsupervised algorithm for both image and video denoising. Our solution is based on a flexible statistical mixture model driven by a finite mixtures of bounded generalized Gaussian distributions (BGGMD) which offers more flexibility in data modeling than the well known classical gaussian distributions which fail to fit the shape of heavy-tailed data produced by the presence of noise or outliers. The proposed framework takes into account also spatial information between neighboring pixels to be more robust and flexible, and able to provide smooth and accurate denoising results. For model’s parameters estimation, we investigate the unsupervised expectation-maximization (EM) algorithm. In order to evaluate the performance of the proposed model, we conducted a series of extensive experiments. Obtained results are more encouraging than those obtained using similar approaches. These results show the robustness and flexibility of the proposed method to adapt different shapes of observed data and bounded support data in the case of noisy images and videos.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Aguerrebere C, Almansa A, Delon J, Gousseau Y, Musé P (2017) A bayesian hyperprior approach for joint image denoising and interpolation, with an application to hdr imaging. IEEE Trans Comput Imaging 3:633–646
Boulanger J, Kervrann C, Bouthemy P (2007) Space-time adaptation for patch-based image sequence restoration. IEEE Trans Pattern Anal Mach Intell 29 (6):1096–1102
Buades A, Lisani JL, Miladinović M (2016) Patch-based video denoising with optical flow estimation. IEEE Trans Image Process 25(6):2573–2586
Cao Y, Luo Y, Yang S (2008) Image denoising with gaussian mixture model. In: Congress on image and signal processing, 2008, CISP’08, vol 3, pp 339–343
Chen Y, Au O, Fan X, Guo L, Wong PH (2007) Maximum a posteriori based (mapbased) video denoising via rate distortion optimization. In: 2007 IEEE international conference on multimedia and expo. IEEE, pp 1930–1933
Cho D, Bui TD (2005) Multivariate statistical approach for image denoising. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, 2005, (ICASSP05), vol 4, pp iv–589
Cong-Hua X, Jin-Yi C, Wen-Bin X (2014) Medical image denoising by generalised gaussian mixture modelling with edge information. IET Image Process 8 (8):464–476
Elguebaly T, Bouguila N (2010) Bayesian learning of generalized gaussian mixture models on biomedical images. In: Proceedings of the 4th IAPR TC3 workshop on artificial neural networks in pattern recognition, ANNPR 2010, april 11-13, 2010. Cairo, Egypt, pp 207–218
Elguebaly T, Bouguila N (2011) Bayesian learning of finite generalized gaussian mixture models on images. Signal Process 91(4):801–820
Fan SKS, Lin Y (2009) A fast estimation method for the generalized gaussian mixture distribution on complex images. Comput Vis Image Underst 113(7):839–853
Goossens B, Piˇzurica A, Philips W (2009) Image denoising using mixtures of projected gaussian scale mixtures. IEEE Trans Image Process 18(8):1689–1702
Hotz T, Marnitz P, Stichtenoth R, Davies L, Kabluchko Z, Munk A (2012) Locally adaptive image denoising by a statistical multiresolution criterion. Comput Stat Data Anal 56(3):543–558
Lindblom J, Samuelsson J (2003) Bounded support gaussian mixture modeling of speech spectra. IEEE Trans Speech Audio Process 11(1):88–99
López-Rubio E, Florentín-núñez MN (2011) Kernel regression based feature extraction for 3d mr image denoising. Med Image Anal 15(4):498–513
Luo E, Chan SH, Nguyen TQ (2016) Adaptive image denoising by mixture adaptation. IEEE Trans Image Process 25(10):4489–4503
Maggioni M, Boracchi G, Foi A, Egiazarian K (2012) Video denoising, deblocking, and enhancement through separable 4-d nonlocal spatiotemporal transforms. IEEE Trans Image Process 21(9):3952–3966
McLachlan G, Peel D (2004) Finite mixture models. Wiley, Hoboken
Meignen S, Meignen H (2006) On the modeling of small sample distributions with generalized gaussian density in a maximum likelihood framework. IEEE Trans Image Process 15(6):1647–1652
Najar F, Bourouis S, Bouguila N, Belguith S (2017) A comparison between different gaussian-based mixture models. In: 14th IEEE international conference on computer systems and applications. IEEE, Tunisia
Ndajah P, Kikuchi H, Yukawa M, Watanabe H, Muramatsu S (2011) An investigation on the quality of denoised images. Intern J Circuits Syst Signal Process 5 (4):423–434
Nguyen TM, Wu QJ, Zhang H (2014) Bounded generalized gaussian mixture model. Pattern Recogn 47(9):3132–3142
Niknejad M, Rabbani H, Babaie-Zadeh M (2015) Image restoration using gaussian mixture models with spatially constrained patch clustering. IEEE Trans Image Process 24(11):3624–3636
Pi M (2006) Improve maximum likelihood estimation for subband {GGD} parameters. Pattern Recogn Lett 27(14):1710–1713
Portilla J, Strela V, Wainwright MJ, Simoncelli EP (2003) Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Trans Image Process 12 (11):1338–1351
Rajni R, Anutam A (2014) Image denoising techniques-an overview. Int J Comput Appl 86(16):13–17
Rajpoot N, Butt I (2012) A multiresolution framework for local similarity based image denoising. Pattern Recogn 45(8):2938–2951
Sattar F, Floreby L, Salomonsson G, Lovstrom B (1997) Image enhancement based on a nonlinear multiscale method. IEEE Trans Image Process 6(6):888–895
Scheunders P, De Backer S (2007) Wavelet denoising of multicomponent images using gaussian scale mixture models and a noise-free image as priors. IEEE Trans Image Process 16(7):1865–1872
Schwenker F, El Gayar N (2010) Artificial neural networks in pattern recognition. In: Proceedings of the 4th IAPR TC3 workshop, ANNPR 2010, April 11-13, 2010, vol 5998. Springer, Cairo, Egypt
Sefidpour A, Bouguila N (2012) Spatial color image segmentation based on finite non-gaussian mixture models. Expert Syst Appl 39(10):8993–9001
Teodoro AM, Almeida MS, Figueiredo MA (2015) Single-frame image denoising and inpainting using gaussian mixtures. In: ICPRAM (2), pp 283–288
Varghese G, Wang Z (2010) Video denoising based on a spatiotemporal gaussian scale mixture model. IEEE Trans Circuits Syst Video Technol 20(7):1032–1040
Wang YQ, Morel JM (2013) Sure guided gaussian mixture image denoising. SIAM J Imag Sci 6(2):999–1034
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13 (4):600–612
Yang HY, Wang XY, Qu TX, Fu ZK (2011) Image denoising using bilateral filter and gaussian scale mixtures in shiftable complex directional pyramid domain. Comput Electr Eng 37(5):656–668
Zhang R, Bouman CA, Thibault JB, Sauer KD (2013) Gaussian mixture markov random field for image denoising and reconstruction. In: Global conference on signal and information processing (globalSIP), 2013 IEEE, pp 1089–1092
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Channoufi, I., Bourouis, S., Bouguila, N. et al. Image and video denoising by combining unsupervised bounded generalized gaussian mixture modeling and spatial information. Multimed Tools Appl 77, 25591–25606 (2018). https://doi.org/10.1007/s11042-018-5808-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-018-5808-9