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Decomposable Pixel Filter Algorithm for Multispectral Satellite Image Denoising
Published on May 2015 by
Pragati Jha,
G.r.sinha
| National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering |
| Foundation of Computer Science USA |
| ACEWRM2015 - Number 3 |
| May 2015 |
| Authors: Pragati Jha, G.r.sinha |
| 5f4d04cf-8a0d-46ff-8958-7c1bbea1ebad |
Pragati Jha, G.r.sinha . Decomposable Pixel Filter Algorithm for Multispectral Satellite Image Denoising. National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering. ACEWRM2015, 3 (May 2015), 19-24.
@article{
author = {
Pragati Jha,
G.r.sinha
},
title = { Decomposable Pixel Filter Algorithm for Multispectral Satellite Image Denoising },
journal = {
National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
},
issue_date = { May 2015 },
volume = { ACEWRM2015 },
number = { 3 },
month = { May },
year = { 2015 },
issn = 0975-8887,
pages = {
19-24
},
numpages = 6,
url = {
/proceedings/acewrm2015/number3/20913-6045/
},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
%A Pragati Jha
%A G.r.sinha
%T Decomposable Pixel Filter Algorithm for Multispectral Satellite Image Denoising
%J National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
%@ 0975-8887
%V ACEWRM2015
%N 3
%P 19-24
%D 2015
%I International Journal of Computer Applications
Abstract
The multispectral images (MSI) convey high definition and authentic representation of the real world in comparison with the RGB or gray-scale images. MSI images help improve the performance measures for image processing and related information encoding tasks. Although, MSI images are often prone to corruption by various sources of noises either while procuring the images or during transmission. This paper studies an innovative MSI de-noising technique which is based on learning based morphology of bidirectional recurrent neural network. The algorithm used in the technique filters the inhomogeneous noisy pixels and the neighboring pixel bands with the noisy patches are corrected accordingly.
References
- M. Aharon, M. Elad, and A. Bruckstein. K-svd: An algorithm for designing over complete dictionaries for sparse representation. IEEE Trans. Signal Processing, 54(11):4311–4323, 2006.
- B. Aiazzi, L. Alparone, A. Barducci, S. Baronti, and I. Pippi: Information theoretic assessment of sampled hyper spectral imagers. IEEE Trans. Geo science and Remote Sensing, 39(7):1447–1458, 2001.
- D. Arthur and S. Vassilvitskii: K-means++: the advantages of careful seeding. Society for Industrial and Applied Mathematics.
- A. Buades, B. Coll, and J. M. Morel: A non-local algorithm for image denoising. In ICVPR, 2005.
- P. D. Burns: Analysis of Image Noise in Multispectral Color Aquisition. PhD thesis, Center for Imaging Science, Rochester Institute of Technology, 1997.
- C. F. Caiafa and A. Cichocki: Computing sparse representations of multi dimensional signals using kronecker bases. Neural Computation, 25(1):186–220, 2013.
- J. D. Carroll and J. J. Chang: Analysis of individual differences in multi dimensional scaling via an n-way generalization of eckart-young decomposition. Psychometrika, 35(3):283–319, 1970.
- A. Chen: The in-painting of hyper spectral images: a survey and adaptation to hyper spectral data. In SPIE, 2012.
- K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian: Image denoising by sparse3d transform-domain collaborative filtering. IEEE Trans. Image Processing, 16(8):2080–2094, 2007.
- L. De Lathauwer, B. De Moor, and J. Vandewalle: On the best rank-1 andrank-(r1, r2, • • • ,rn) approximation of higher-order tensors. SIAM Journal on Matrix Analysis and Applications, 21(4):1324–1342, 2000.
- D. H. Foster, K. Amano, S. M. C. Nascimento, and M. J. Foster: Frequency of metamerism in natural scenes. Journal of the Optical Society of America A, 23(10):2359–2372, 2006.
- R. Kawakami, J. Wright, Y. W. Tai, Y. Matsushita, and M. K. Ikeuchi: High resolution hyper spectral imaging via matrix factorization. In CVPR, 2011.
- J. Kerekes and J. Baum: Full-spectrum spectral imaging system analytical model. IEEE Trans. Geo science and Remote Sensing, 5(2):571–580, 2005.
- X. F. Liu, S. Bourennane, and C. Fossati: Denoising of hyper spectral images using the parafac model and statistical performance analysis. IEEE Trans. Geo science and Remote Sensing, 50(10):3717–3724, 2012.
- M. Maggioni and A. Foi: Nonlocal transform-domain denoising of volumetric data with group wise adaptive variance estimation. SPIE 2012.
- K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian: Image denoising by sparse 3D transform-domain collaborative filtering, IEEE Trans. Image Process. , 16 (2007), pp. 2080-2095.
- S. Lansel, Denoise Lab: http://www. stanford. edu/~slansel/DenoiseLab.
- V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola: From local kernel to nonlocal multiple-model image denoising, International Journal of Computer Vision, 86 (2010), pp. 1-32.
- E. Vansteenkiste, D. Van der Weken, W. Philips, and E. Kerre: Perceived image quality measurement of state-of-the-art noise reduction schemes, in Lecture Notes in Computer Science ACIVS, vol. 4179, Antwerp, Belgium, Sep. 2006, pp. 114-124.
- K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian: Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space, in Proc. IEEE Int. Conf. Image Process. , vol. 1, Sep. 2007, pp. 313-316.
- J. Starck, E. Candes, and D. Donoho: "The curvelet transform for image denoising," IEEE Trans. Image Proc. 11, pp. 670 {684, June 2002.
- J. A. Saghri and A. G. Tescher: Near-lossless bandwidth compression for radiometric data, Optical Engineering, 30 (1991), pp. 934-939.
- B. Epstein, R. Hingorani, J. Shapiro, and M. Czigler: Multispectral klt-wavelet data compression for landsat thematic mapper images, in Data Compression Conference, 1992. DCC '92. , Mar 1992, pp. 200-208.
- D. Tretter and C. Bouman: Optimal transforms for multispectral and multilayer image coding, Image Processing, IEEE Transactions on, 4 (1995), pp. 296-308.
- Rai A. A Novel Decomposable Pixel Component Analysis Algorithm for Automating Multispectral Satellite Image Denoising. Research & Reviews: A Journal of Embedded System & Applications. 2014; 2(3): 18–25p. .
- A. Foi, M. Trimeche, V. Katkovnik, and K. Egiazarian: Practical poissonian-gaussian noise modeling and fitting for single-image raw-data, Image Processing, IEEE Transactions on, 17 (2008), pp. 1737-1754.
- Rai A. Multispectral Image Denoising Using Bi-Directional Recurrent Neural Network with DPCA Algorithm. Journal of Image Processing & Pattern Recognition Progress. 2015; 2(1): 25–30p.
- S. Hordley, G. Finalyson, and P. Morovic: A multi-spectral image database and its application to image rendering across illumination, in Image and Graphics, 2004. Proceedings Third International Conference on, Dec. 2004, pp. 394-397.
- G. Finlayson, S. Hordley, and P. Morovic: Using the Spectra Cube to build a multispectral image database, in Proc. Second European Conference on Color in Graphics, Imaging and Vision, CGIV 2004,Aachen, Germany, April 2004, pp. 268-274.
- Rai. A, Attribute Based Level Adaptive Thresholding Algorithm (ABLATA) for Image Compression and Transmission, Vol 12, Issue 3, 2014, pp: 211-218.
- G R Sinha, Ravindra Ramteke, Vikas Dilliwar: Implementation of Image Denoising algorithm for Additive Noise using MATLAB, IJERIA, Vol 2, No II, pp: 105-113 (2009)
- G R Sinha: Assessment of Image Restoration Techniques to Remote Sensing Applications, i-manager`s Journal on Future Engineering and Technology, Vol 5, No 3, pp: 33-37, Feb-Apr Issue (2010)
Index Terms
Computer Science
Information Sciences
Keywords