Articles
Comparison and evaluation of noise suppression methods in astronomical images using Wavelets
Published 2010-12-15
Keywords
- Digital image processing,
- astronomical images,
- wavelet transformation,
- noise supression,
- gaussian noise
How to Cite
Basto Pineda, J. C., & Plata Gómez, A. (2010). Comparison and evaluation of noise suppression methods in astronomical images using Wavelets. Revista UIS Ingenierías, 9(2), 227–235. Retrieved from https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/2071
Abstract
In this paper, several wavelet-based techniques for removal of gaussian noise in astronomical images are compared. Validation experiments were carried out with previously processed images from the SDSS, by adding noise artificially and comparing the effectiveness of each method in the reconstruction of original image by means of quantitative indicators like the mean square error and the signal to noise ratio. Results obtained with some of used methods are quite satisfactory. Also, their comparison with other papers shows contradictory conclutions about the best option among the considered methods. Authors of those papers made their validation experiments with another kind of images of non-astronomical origin, having therefore other characteristics.
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References
T. Celik, K-K Ma. “Multitemporal Image Change Detection Using Undecimated Discrete Wavelet Transform and Active Contours”. Geoscience and Remote Sensing, IEEE Transactions on, Vol 49, 2011-2. p. 706.
G. Chopra, A.Ñ. Pal. “An Improved Image Compression Algorithm Using Binary Space Partition Scheme and Geometric Wavelets”. Image processing, IEEE Transactions on, Vol 20, 2011-1. p. 270.
G.Chen, S.-E. Qian. “Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage”. Geoscience and Remote Sensing, IEEE Transactions on, Vol 48, 2010-12. p. 1.
G. Plonka, S. Tenorth , D. Rosca. “A New Hybrid Method for Image Approximation Using the Easy Path Wavelet Transform”. Image processing, IEEE Transactions on, Vol 20, 2011-2. p. 372.
X. Yang, Y. Shi, B. Yang. “General framework of the construction of biorthogonal wavelets based on bernstein bases: theory analysis and application in image compression” Computer Vision, IET, Vol 5, 2011-1. p. 50.
N. Mitianoudis, G. Tzimiropoulos, T. Stathaki. “ Fast wavelet-based pansharpening of multi-spectral images”. Imaging Systems and Techniques (IST), 2010 IEEE International Conference on. Thessaloniki, 2010. p. 11.
Hong Yang, Yiding Wang. “An Improved Method of Wavelets Basis Image Denoising Using Besov Norm Regularization”. Fourth International Conference on Image and Graphics. IEEE Computer society, 2007.
M. Tello Alonso, C. López-Martínez, J.J. Mallorquí, P. Salembier. “Edge Enhancement Algorithm Based on the Wavelet Transform for Automatic Edge Detection in SAR Images”. Geoscience and Remote Sensing, IEEE Transactions on, Vol 49, 2011-1. p. 222.
J.L. Starck, F. Murtagh. Astronomical image and data analysis. Berlin. 2nd Edition, Springer-Verlag. 2006. pp. 29–56 / 291–296.
M.E. Zervakis, V. Sundararajan, K.K. Parhi. “A wavelet-domain algorithm for denoising in the presence of noise outliers”. 1997 International Conference on Image Processing (ICIP’97) - Volume 1. Washington, DC.
P. Ravier, P.-O. Amblard. “Denoisin using wavelet packets and the kurtosis: application to the transient detection”. Time-Frequency and Time-Scale Analysis, 1998. Proceedings of the IEEE-SP International Symposium on. pp. 625 – 628. Pittsburgh.
S. Dattaprasad, R. Pieper, M. Shirvaikar, “Restoration of color images using wavelets,” ssst, pp.447-451, Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005. SSST ‘05., 2005.
Li Wenzhe, Lin Ji-Nan, R. Unbehauen, “Wavelet based nonlinear image enhancement for Gaussian and uniform noise,” icip, vol. 1, pp.550, 1998 International Conference on Image Processing (ICIP’98) - Volume 1, 1998.
J.C Basto. “Caracterización del ruido gaussiano presente en las imágenes del proyecto Sloan Digital Sky Survey e implementación de algoritmos de supresión de ruido utilizando wavelets”. Memorias del I Congreso Colombiano de Astronomía y Astrofísica. UdeA. 2008. pp. 93-96.
H. Zheng-Hong, L. Xia. “Image Denoising for Adaptive Threshold Function Based on the Dyadic Wavelet Transform”. Proceeding of the International Conference on Electronic computer Technology. China. 2009. pp. 147–150.
H. Zheng-Hong, H. Xi-Ping, B. Fang, L. Xia. “Image denoising and comparison by improving threshold based on the dyadic wavelet transform”. Proceeding of the International conference on Wavelet Analysis and Pattern Recognition. Beijing. 2007. pp. 535-539.
Zhang Wei-Qiang, Song Guo-Xiang. “Signal de-noising in wavelet domain based on a new kind of thresholding function”. Journal of Xidian University, Vol 4, 2004-02. pp. 296-299.
David L Donoho. “Denoising by soft thresholding”. IEEE Trans. Information Theory, 41(3), May 1995, pp.613-627.
D. Donoho and I. Johnstone, “Ideal spatial adaptation via wavelet shrinkage”. Biometrika, vol. 81, 1994, pp.425- 455.
G. Chopra, A.Ñ. Pal. “An Improved Image Compression Algorithm Using Binary Space Partition Scheme and Geometric Wavelets”. Image processing, IEEE Transactions on, Vol 20, 2011-1. p. 270.
G.Chen, S.-E. Qian. “Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage”. Geoscience and Remote Sensing, IEEE Transactions on, Vol 48, 2010-12. p. 1.
G. Plonka, S. Tenorth , D. Rosca. “A New Hybrid Method for Image Approximation Using the Easy Path Wavelet Transform”. Image processing, IEEE Transactions on, Vol 20, 2011-2. p. 372.
X. Yang, Y. Shi, B. Yang. “General framework of the construction of biorthogonal wavelets based on bernstein bases: theory analysis and application in image compression” Computer Vision, IET, Vol 5, 2011-1. p. 50.
N. Mitianoudis, G. Tzimiropoulos, T. Stathaki. “ Fast wavelet-based pansharpening of multi-spectral images”. Imaging Systems and Techniques (IST), 2010 IEEE International Conference on. Thessaloniki, 2010. p. 11.
Hong Yang, Yiding Wang. “An Improved Method of Wavelets Basis Image Denoising Using Besov Norm Regularization”. Fourth International Conference on Image and Graphics. IEEE Computer society, 2007.
M. Tello Alonso, C. López-Martínez, J.J. Mallorquí, P. Salembier. “Edge Enhancement Algorithm Based on the Wavelet Transform for Automatic Edge Detection in SAR Images”. Geoscience and Remote Sensing, IEEE Transactions on, Vol 49, 2011-1. p. 222.
J.L. Starck, F. Murtagh. Astronomical image and data analysis. Berlin. 2nd Edition, Springer-Verlag. 2006. pp. 29–56 / 291–296.
M.E. Zervakis, V. Sundararajan, K.K. Parhi. “A wavelet-domain algorithm for denoising in the presence of noise outliers”. 1997 International Conference on Image Processing (ICIP’97) - Volume 1. Washington, DC.
P. Ravier, P.-O. Amblard. “Denoisin using wavelet packets and the kurtosis: application to the transient detection”. Time-Frequency and Time-Scale Analysis, 1998. Proceedings of the IEEE-SP International Symposium on. pp. 625 – 628. Pittsburgh.
S. Dattaprasad, R. Pieper, M. Shirvaikar, “Restoration of color images using wavelets,” ssst, pp.447-451, Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005. SSST ‘05., 2005.
Li Wenzhe, Lin Ji-Nan, R. Unbehauen, “Wavelet based nonlinear image enhancement for Gaussian and uniform noise,” icip, vol. 1, pp.550, 1998 International Conference on Image Processing (ICIP’98) - Volume 1, 1998.
J.C Basto. “Caracterización del ruido gaussiano presente en las imágenes del proyecto Sloan Digital Sky Survey e implementación de algoritmos de supresión de ruido utilizando wavelets”. Memorias del I Congreso Colombiano de Astronomía y Astrofísica. UdeA. 2008. pp. 93-96.
H. Zheng-Hong, L. Xia. “Image Denoising for Adaptive Threshold Function Based on the Dyadic Wavelet Transform”. Proceeding of the International Conference on Electronic computer Technology. China. 2009. pp. 147–150.
H. Zheng-Hong, H. Xi-Ping, B. Fang, L. Xia. “Image denoising and comparison by improving threshold based on the dyadic wavelet transform”. Proceeding of the International conference on Wavelet Analysis and Pattern Recognition. Beijing. 2007. pp. 535-539.
Zhang Wei-Qiang, Song Guo-Xiang. “Signal de-noising in wavelet domain based on a new kind of thresholding function”. Journal of Xidian University, Vol 4, 2004-02. pp. 296-299.
David L Donoho. “Denoising by soft thresholding”. IEEE Trans. Information Theory, 41(3), May 1995, pp.613-627.
D. Donoho and I. Johnstone, “Ideal spatial adaptation via wavelet shrinkage”. Biometrika, vol. 81, 1994, pp.425- 455.