Artículos
Comparación y evaluación de métodos de supresión de ruido en imágenes de origen astronómico utilizando Wavelets
Publicado 2010-12-15
Palabras clave
- Procesamiento digital de imágenes,
- mágenes astronómicas,
- transformaciones wavelets,
- supresión de ruido,
- ruido gaussiano
Cómo citar
Basto Pineda, J. C., & Plata Gómez, A. (2010). Comparación y evaluación de métodos de supresión de ruido en imágenes de origen astronómico utilizando Wavelets. Revista UIS Ingenierías, 9(2), 227–235. Recuperado a partir de https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/2071
Resumen
En este artículo se comparan diferentes técnicas de supresión de ruido gaussiano en imágenes de origen astronómico, todas ellas basadas en filtrado de coeficientes mediante transformaciones wavelet. Se realizaron experimentos de validación con imágenes previamente tratadas provenientes del SDSS1, añadiendo ruido artificialmente y comparando la efectividad de cada método en la reconstrucción de la señal original, mediante indicadores cuantitativos como el error medio cuadrático y la relación señal a ruido. Los resultados obtenidos con algunos de los métodos son muy satisfactorios. Adicionalmente, se debe mencionar que esta comparación lleva a conclusiones contradictorias con otros artículos sobre el tema. Los autores de dichos artículos utilizaron imágenes de origen no astronómico, y por tanto con características distintas.
Descargas
Los datos de descargas todavía no están disponibles.
Referencias
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.