Image Denoising with Modified Grey Wolf Optimizer

Yıl 2018, Cilt: 6 Sayı: 4, 962 - 982, 01.08.2018

Hüseyin Avni Ardaç Pakize Erdoğmuş

https://doi.org/10.29130/dubited.435783

Cited By: 1

Öz

In this study, image denoising has
been realized with with the one of the recent Nature-Inspired optimization
algorithms, Grey Wolf Optimizer(GWO). GWO is one of the recent most studied
continous optimization algorithm which performs better than the other
algorithms. In this study, ten test images have been selected and gaussian  noise has been added  with some variance values.  After the noisy images have been attained,
these noisy images have been filtered with convulation in spatial domain. Filter
coefficents have been trained with GWO, Modified Grey Wolf Optimizer(MGWO) and
Genetic Algorithm(GA). Weiner filtering is also applied on the images for image
denosing. The results show that Weiner Filter outperforms GWO trained filters
on most of the images.  MGWO performance
is better then GWO and the results show that MGWO can also be used as an
alternative method for image denoising. In the future studies, adaptive MGWO
can be enhanced for much more succesfull image denoising process.

Anahtar Kelimeler

GWO, MGWO, GA, Image Denoising

Düzenlenmiş Gri Kurt Optimizasyon Algoritması ile Gürültü Temizleme

Öz

Bu çalışmada, yakın
zamanda doğadan esinlenen optimizasyon algoritmalarından biri olan Gri Kurt
Optimizasyonu(GWO) ile görüntülerdeki gürültülerin temizlenmesi
gerçekleştirilmiştir. GWO, diğer algoritmalardan daha iyi performans gösteren,  son zamanlarda en çok çalışılan sürekli
optimizasyon algoritmasından biridir. Bu çalışmada on test görüntüsü seçilmiş
ve bazı varyans değerleri ile gauss gürültüsü eklenmiştir. Gürültülü görüntüler
elde edildikten sonra, bu gürültülü görüntüler  uzamsal  alanda konvülasyon ile filtrelenmiştir. Filtre
katsayıları GWO, Modifiye Gri Kurt Optimizasyonu (MGWO) ve Genetik Algoritma
(GA) ile eğitilmiştir. Elde edilen sonuçlara göre Weiner filter çoğu resimde
daha başarılı sonuçlar vermiştir.  MGWO’nun performansı GWO’dan daha iyidir ve
sonuçlar MGWO’nun gürültü gidermede alternatif bir metot olarak
kullanılabileceğini göstermiştir. Gelecekteki çalışmalarda daha başarılı gürültü
temizleme işlemi için adaptif MGWO geliştirilebilir.

Anahtar Kelimeler

GWO, MGWO, GA, Gürültü Temizleme

Kaynakça

• D. K. Priya, B. B. Sam, S. Lavanya and A. P. Sajin, "A survey on medical image denoising using optimisation technique and classification," 2017 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, 2017, pp. 1-6.
• D. Chowdhury, S. Gupta, D. Roy, D. Sarkar, C. C. Chattopadhyay and S. K. Das, "A quantum study on digital image noises and their in-depth clusterization," 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, 2017, pp. 1-7.
• Manoj Diwakar, Manoj Kumar, A review on CT image noise and its denoising, Biomedical Signal Processing and Control, Volume 42, 2018,Pages 73-88, ISSN 1746-8094,
• B. Gupta and S. Singh Negi, “Image Denoising with Linear and Non-Linear Filters: A Review”, IJCSI International Journal of Computer Science Issues, ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784 www.IJCSI.org, vol. 10, Issue 6, no. 2, (2013) November.
• Patidar, P. K., Singh, B., and Bagaria, G. (2014). Image filtering using linear and non linear filter for gaussian noise. International Journal of Computer Applications, 93(8).
• Mr. Vijay R. Tripathi, “Image Denoising Using Non Linear Filters,” Int. J. of Computer and Communications ,Vol. 1, No. 1, March 2011.
• Feng Liu, Jingbo Liu, Anisotropic diffusion for image denoising based on diffusion tensors, Journal of Visual Communication and Image Representation, Volume 23, Issue 3,2012, Pages 516-521,ISSN 1047-3203.
• H. Kim and S. Kim, "Impulse-mowing anisotropic diffusion filter for image denoising," 2014 IEEE International Conference on Image Processing (ICIP), Paris, 2014, pp. 2923-2927.doi: 10.1109/ICIP.2014.7025591
• L. Chato, S. Latifi and P. Kachroo, "Total variation denoising method to improve the detection process in IR images," 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York City, NY, 2017, pp. 441-447.
• Y. Li, S. Ding, Z. Li, X. Li and B. Tan, "Dictionary learning in the analysis sparse representation with optimization on Stiefel manifold," 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, 2017, pp. 1270-1274.
• J. Maggu, R. Hussein, A. Majumdar and R. Ward, "Impulse denoising via transform learning," 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, 2017, pp. 1250-1254.
• Ahmed Ben Said, Rachid Hadjidj, Kamal Eddine Melkemi, Sebti Foufou, Multispectral image denoising with optimized vector non-local mean filter, Digital Signal Processing, Volume 58, 2016, Pages 115-126, ISSN 1051-2004.
• Siddhartha Bhattacharyya, Pankaj Pal, Sandip Bhowmick, Binary image denoising using a quantum multilayer self organizing neural network, Applied Soft Computing, Volume 24, 2014,Pages 717-729, ISSN 1568-4946.
• Christos Ferles, Yannis Papanikolaou, Kevin J. Naidoo, Denoising Autoencoder Self-Organizing Map (DASOM), Neural Networks, Volume 105, 2018, Pages 112-131,ISSN 0893-6080.
• A. Benou, R. Veksler, A. Friedman, T. Riklin Raviv,Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences,Medical Image Analysis,Volume 42,2017,Pages 145-159,ISSN 1361-8415.
• Donghoon Lee, Sunghoon Choi, Hee-Joung Kim, Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 884, 2018, Pages 97-104, ISSN 0168-9002.
• Donghoon Lee, Sunghoon Choi, Hee-Joung Kim, Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 884, 2018, Pages 97-104, ISSN 0168-9002.
• X. Kuang, X. Sui, Y. Liu, Q. Chen and G. GU, "Single Infrared Image Optical Noise Removal Using a Deep Convolutional Neural Network," in IEEE Photonics Journal, vol. 10, no. 2, pp. 1-15, April 2018.
• M. F. Fahmy and O. M. Fahmy, "A new image denoising technique using orthogonal complex wavelets," 2018 35th National Radio Science Conference (NRSC), Cairo, 2018, pp. 223-230. doi: 10.1109/NRSC.2018.8354367
• M. Kimlyk and S. Umnyashkin, "Image denoising using discrete wavelet transform and edge information," 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow, 2018, pp. 1823-1825.
• T. Williams and R. Li, "An efficient hybrid Fourier-Wavelet Neighborhood Coefficient image denoising approach," SoutheastCon 2016, Norfolk, VA, 2016, pp. 1-4. doi: 10.1109/SECON.2016.7506761
• J. Fan, H. Yi, L. Xu and T. Zhao, "A Histogram-Based Denoising Algorithm in a Joint-Fourier Transform Correlator for Image Recognition," 2012 Symposium on Photonics and Optoelectronics, Shanghai, 2012, pp. 1-3.
• S. K. Panigrahi, S. Gupta and P. K. Sahu, "Curvelet-based multiscale denoising using non-local means & guided image filter," in IET Image Processing, vol. 12, no. 6, pp. 909-918, 6 2018.
• S. Zhen-gang and L. Qin-zi, "Pulmonary CT image denoising algorithm based on curvelet transform criterion," 2017 7th IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies (MAPE), Xi'an, 2017, pp. 520-524.
• Q. Zhao, B. Ye, X. Wang and D. Zhou, "Mixed image denoising method of non-local means and adaptive bayesian threshold estimation in NSCT domain," 2010 3rd International Conference on Computer Science and Information Technology, Chengdu, 2010, pp. 636-639.
• S. Fei and R. Zhao, "Adaptive Wavelet Shrinkage For Image Denoising Based On SURE Rule," 2006 8th international Conference on Signal Processing, Beijing, 2006, pp.
• Y. Q. Mohsin, G. Ongie and M. Jacob, "Iterative Shrinkage Algorithm for Patch-Smoothness Regularized Medical Image Recovery," in IEEE Transactions on Medical Imaging, vol. 34, no. 12, pp. 2417-2428, Dec. 2015.
• Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis, Grey Wolf Optimizer, In Advances in Engineering Software, Volume 69, 2014, Pages 46-61, ISSN 0965-9978, https://doi.org/10.1016/j.advengsoft.2013.12.007.
• T. I. Singh, R. Laishram and S. Roy, "Image segmentation using spatial fuzzy C means clustering and grey wolf optimizer," 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, 2016, pp. 1-5.doi: 10.1109/ICCIC.2016.7919713
• Mirjalili, S. Appl Intell (2015) 43: 150. https://doi.org/10.1007/s10489-014-0645-7
• D. Jitkongchuen and P. Phaidang, "Grey wolf algorithm with borda count for feature selection in classification," 2018 3rd International Conference on Control and Robotics Engineering (ICCRE), Nagoya, Japan, 2018, pp. 238-242.
• E. Daniel, "Optimum Wavelet Based Homomorphic Medical Image Fusion Using Hybrid Genetic – Grey Wolf Optimization Algorithm," in IEEE Sensors Journal.doi: 10.1109/JSEN.2018.2822712
• Y. L. Karnavas and I. D. Chasiotis, "PMDC coreless micro-motor parameters estimation through Grey Wolf Optimizer," 2016 XXII International Conference on Electrical Machines (ICEM), Lausanne, 2016, pp. 865-870.
• Antoni Buades, Bartomeu Coll, Jean-Michel Morel. A review of image denoising algorithms, with a new one. SIAM Journal on Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, 2005, 4 (2), pp.490-530.
• Chao Lu, Shengqiang Xiao, Xinyu Li, Liang Gao, An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production, In Advances in Engineering Software, Volume 99, 2016, Pages 161-176, ISSN 0965-9978, https://doi.org/10.1016/j.advengsoft.2016.06.004.
• T. Jayabarathi, T. Raghunathan, B.R. Adarsh, Ponnuthurai Nagaratnam Suganthan, Economic dispatch using hybrid grey wolf optimizer, In Energy, Volume 111, 2016, Pages 630-641, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2016.05.105.
• Kamboj, V.K. Neural Comput & Applic (2016) 27: 1643. https://doi.org/10.1007/s00521-015-1962-4
• Soni G. Parmar M. Kumar S. Panda "Hybrid Grey Wolf Optimization-Pattern Search (hGWO-PS) Optimized 2DOF-PID Controllers for Load Frequency Control (LFC) in Interconnected Thermal Power Plants" <em>ICTACT Journal on Soft Computing</em> vol. 06 no. 03 pp. 1244-1256 April 2016.
• Ali Asghar Heidari, Parham Pahlavani, An efficient modified grey wolf optimizer with Lévy flight for optimization tasks, In Applied Soft Computing, Volume 60, 2017, Pages 115-134, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2017.06.044.
• Nitin Mittal, Urvinder Singh, and Balwinder Singh Sohi, “Modified Grey Wolf Optimizer for Global Engineering Optimization,” Applied Computational Intelligence and Soft Computing, vol. 2016, Article ID 7950348, 16 pages, 2016. https://doi.org/10.1155/2016/7950348.
• Ijjina, E. P., and Chalavadi, K. M. (2016). Human action recognition using genetic algorithms and convolutional neural networks. Pattern Recognition,59, pp. 199-212. http://dx.doi.org/10.1016/j.patcog.2016.01.012.
• Pakize Erdoğmuş and Simge Ekiz. Nonlinear Regression using Particle Swarm Optimization and Genetic Algorithm. International Journal of Computer Applications 153(6):28-36, November 2016.