(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-5 Issue-49 December-2018
Full-Text PDF
DOI:10.19101/IJATEE.2018.545023
Paper Title : A hybrid image denoising method based on clustering and PDE
Author Name : Sonal Pandya and Ravindra Gupta
Abstract :

This paper provides an efficient method based on the combination of hierarchical clustering along with the capability of PDE, FFT and color domination. Then for the edge point selection decomposition has been performed. It is applied with the clustering mechanism so that data points are separated. Then by similarity ranking alike data points are separated and decomposed. By this process, noise can be separated and other image proprieties along with the alikeness are separated. The color domination, PDE and FFT combination have been applied. This is applied on the data obtained from the previous process. This step provides the color based separation and error filtration. PSNR values have been used for the comparative study. The obtained results have higher PSNR then the previous approaches shows the effectiveness of our approach.

Keywords : Hybrid method, PDE, FFT, PSNR.
Cite this article : Sonal Pandya and Ravindra Gupta, " A hybrid image denoising method based on clustering and PDE " , International Journal of Advanced Technology and Engineering Exploration (IJATEE), Volume-5, Issue-49, December-2018 ,pp.484-488.DOI:10.19101/IJATEE.2018.545023
References :
[1]Shannon CE. Communication in the presence of noise. Proceedings of the IRE. 1949; 37(1):10-21.
[Crossref] [Google Scholar]
[2]Nyquist H. Certain topics in telegraph transmission theory. Transactions of the American Institute of Electrical Engineers. 1928; 47(2):617-44.
[Crossref] [Google Scholar]
[3]Candes EJ, Wakin MB. An introduction to compressive sampling. IEEE Signal Processing Magazine. 2008; 25(2):21-30.
[Crossref] [Google Scholar]
[4]Ghosh P, Pandey A, Pati UC. Comparison of different feature detection techniques for image mosaicing. ACCENTS Transactions on Image Processing and Computer Vision. 2015; 1(1):1-7.
[Google Scholar]
[5]Tropp JA, Gilbert AC. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory. 2007; 53(12):4655-66.
[Crossref] [Google Scholar]
[6]Victoria BL, Sathappan S. A survey on impulse noise removal techniques in image processing. International Journal of Advanced Technology and Engineering Exploration. 2018; 5(43):160-4.
[Crossref] [Google Scholar]
[7]Chitra AD, Ponmuthuramalingam P. Face recognition with positive and negative samples using support vector machine. ACCENTS Transactions on Image Processing and Computer Vision. 2016; 2(5): 16-9.
[Crossref] [Google Scholar]
[8]Mohapatra BN, Panda PP. Histogram equalization and noise removal process for enhancement of image. ACCENTS Transactions on Image Processing and Computer Vision. 2017; 3(9): 22-5.
[Crossref] [Google Scholar]
[9]To AC, Moore JR, Glaser SD. Wavelet denoising techniques with applications to experimental geophysical data. Signal Processing. 2009; 89(2):144-60.
[Crossref] [Google Scholar]
[10]TV NP, Hemanth VK, Kumar S, Soman KP, Soman A. Comparative study of recent compressed sensing methodologies in astronomical images. In eco-friendly computing and communication systems 2012 (pp. 108-16). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]
[11]Dubey S, Hasan F, Shrivastava G. A hybrid method for image denoising based on wavelet thresholding and RBF network. International Journal of Advanced Computer Research. 2012; 2(4): 167-72.
[Google Scholar]
[12]Mohideen SK, Perumal SA, Krishnan N, Selvakumar RK. A novel approach for image denoising using dynamic tracking with new threshold technique. In international conference on computational intelligence and computing research 2010 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[13]Benabdelkader S, Soltani O. Wavelet image denoising based spatial noise estimation. In signal processing and intelligent systems conference 2015 (pp. 83-7). IEEE.
[Crossref] [Google Scholar]
[14]Tian J, Chen L. Adaptive image denoising using a non-parametric statistical model of wavelet coefficients. In international symposium on intelligent signal processing and communication systems 2010 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[15]Singh J, Dubey RB. Reduction of noise image using LMMSE. International Journal of Advanced Computer Research. 2012; 2(5):147-52.
[Google Scholar]
[16]Anandan P, Sabeenian RS. Curvelet based image compression using support vector machine and core vector machine-a review. International Journal of Advanced Computer Research. 2014; 4(15):675-81.
[Google Scholar]
[17]Veena PV, Devi GR, Sowmya V, Soman KP. Least square based image denoising using wavelet filters. Indian Journal of Science and Technology. 2016; 9(30):1-6.
[Crossref] [Google Scholar]
[18]Rajoriya R, Patidar K, and Chouhan S. A survey and analysis on color image encryption algorithms. ACCENTS Transactions on Information Security. 2018; 3(9):1-5.
[19]Chithra K, Santhanam T. Hybrid denoising technique for suppressing Gaussian noise in medical images. In international conference on power, control, signals and instrumentation engineering 2017 (pp. 1460-3). IEEE.
[Crossref] [Google Scholar]
[20]Soni N, Kirar K. Transform based image denoising: a review. In international conference on recent innovations in signal processing and embedded systems 2017 (pp. 168-71). IEEE.
[Crossref] [Google Scholar]
[21]Pang J. Improved image denoising based on Haar wavelet transform. In SmartWorld, ubiquitous intelligence & computing, advanced & trusted computed, scalable computing & communications, cloud & big data computing, internet of people and smart city innovation 2017 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[22]Yang W, Liu J. Denoising fluorescence molecular image by k-means clustering. In international conference on computer and communications 2017 (pp. 1847-50). IEEE.
[Crossref] [Google Scholar]
[23]Ankarao V, Sowmya V, Soman KP. Sparse image denoising using dictionary constructed based on least square solution. In international conference on wireless communications, signal processing and networking 2017 (pp. 1165-71). IEEE.
[Crossref] [Google Scholar]
[24]Vyas A, Paik J. Applications of multiscale transforms to image denoising: survey. In international conference on electronics, information, and communication 2018 (pp. 1-3). IEEE.
[Crossref] [Google Scholar]
[25]Liu Z, Yan WQ, Yang ML. Image denoising based on a CNN model. In international conference on control, automation and robotics 2018 (pp. 389-93). IEEE.
[Crossref] [Google Scholar]
[26]Mbarki Z, Seddik H, Braiek EB. Non blind image restoration scheme combining parametric wiener filtering and BM3D denoising technique. In international conference on advanced technologies for signal and image processing 2018 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]