The week's pick
Reseach Article
An Efficient Shearlet Bayesian Network based Approach for Image Denoising
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 128 - Number 10 |
| Year of Publication: 2015 |
| Authors: Anubhuti Sharma, Shaila Chugh |
10.5120/ijca2015906646
|
Anubhuti Sharma, Shaila Chugh . An Efficient Shearlet Bayesian Network based Approach for Image Denoising. International Journal of Computer Applications. 128, 10 ( October 2015), 15-20. DOI=10.5120/ijca2015906646
Abstract
The evolution innermost the processing power of electronic devices direct the research of well-organized image denoising technique in the direction of the extra complex method which use the difficult transform functional test with statistics. Even supposing with the difficulty of the newly developed techniques, generally algorithm fails to whole adorable stage of performance. For the mainly part algorithms fail since the expedient model mismatch the algorithm presumption taken at the time of improvement. This paper presents a proficient approach intended for image denoising based on Shearlet transform and the Bayesian Network. The projected technique use the geometric dependencies in the shearlet domain in the direction of the Bayesian Network which is next used for predict the noise probability. The Shearlet transform provide improved approximation particularly in different scales, and directional discontinuities which make it preferable designed used within support of processing the pixel around the edge. The later result prove that the future technique better wavelet base method visually and mathematical in conditions of PSNR (peak signal -to -noise ratio).
References
- Chengzhi Deng, Saifeng wei Tian “Total Variation Based Multivariate Shearlet Shrinkage For Image Reconstruction”, Institute of Advanced Engineering and science Telkomnika, Vol. 11, January2013, pp. 40-47.
- Miao, Qi-guang, Shi, Cheng “novel algorithm of image fusion using shearlets”,2011 Optics Communications, Volume 284, Issue 6, p. 1540-1547.
- Glenn Easleya, Demetrio Labateb,Wang-Q Limc.“Sparse directional image representations using the discrete shearlet transform” Volume 25, Issue 1, July 2008 Pages 25–46.
- Wubuli, Ayiguli, Zhen-Hong, Jia; Xi-Zhong, Qin; Jie, “Medical Image Enhancement Based on Shearlet Transform and Unsharp Masking”, Journal of Medical Imaging and Health Informatics, Volume 4, Number 5-2014.
- J. Zhao, L. Lü, H. Sun, “Multi-Threshold Image Denoising Based on Shearlet Transform", Applied Mechanics and Materials, Vols. 29-32, pp. 2251-2255, Aug. 2010.
- Chengzhi Deng, Wei Tian, Saifeng Hu, Yan Li, Min Hu, Shengqian Wang, “Shearlet-based image denoising using adaptive thresholding and non-local means filter”, International Journal of Digital Content Technology and its Applications(JDCTA) Volume6,Number20,November 2012.
- Qi-guang Miao, Cheng Shi, “A novel algorithm of image fusion using shearlets”,Volume 284, Issue 6, 15 March 2011, Pages 1540–1547.
- Wang-Q Lim, “Non separable Shearlet Transform”, Image Processing, IEEE Transaction 2013 (Volume: 22 Issue: 5).
- Sheng Yi, Labate, D. ; Easley, G.R. ; Krim, H. “A Shearlet Approach to Edge Analysis and Detection”, Image Processing, IEEE Transactions on (Volume:18, Issue:5) March 2009.
- Junliang Liu; Lin Lei; Shilin Zhou. “Nonsubsampled Shearlet-based image denoising using multiscale products”,
- Intelligent Control and Information Processing (ICICIP), 2013 Fourth International Conference on. Yang-ChengLiua,Hong-YingYanga, “Image denoising in extended Shearlet domain using hidden Markov tree models” Volume 30, July 2014, Pages 101–113.
- Ying Li, Rui-ming, Chen Shi Liang. “A New Image Denoising Method Based on Shearlet Shrinkage and Improved Total Variation”.Volume7202 of the series Lecture Notes in Computer Science pp 382-388
- Ben-Gal I., Ruggeri F., Faltin F. &Kennett R. “Bayesian Networks”, Encyclopedia of Statistics in Quality & Reliability, Wiley & Sons (2007).
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