A Modified Adaptive PCA Learning based Method for Image Denoising
|
10.5120/13025-0103 |
Ghada Mounir Shaker, Alaa A Hefnawy and Moawed I.dessouky. Article: A Modified Adaptive PCA Learning based Method for Image Denoising. International Journal of Computer Applications 74(20):10-18, July 2013. Full text available. BibTeX
@article{key:article,
author = {Ghada Mounir Shaker and Alaa A. Hefnawy and Moawed I.dessouky},
title = {Article: A Modified Adaptive PCA Learning based Method for Image Denoising},
journal = {International Journal of Computer Applications},
year = {2013},
volume = {74},
number = {20},
pages = {10-18},
month = {July},
note = {Full text available}
}
Abstract
The paper deals with image denoising with a new approach towards obtaining high quality denoised image patches using only a single image. A learning technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. this paper show a framework for denoising by learning an appropriate basis function to describe image patches after applying transform domain method on noisy image patches. Such basis functions are used to describe geometric structure. The algorithm maps have been applies on LR patch space to generate the HR one, generating HR patch. Using this strategy, more patch patterns can be represented using a smaller training database. In super resolution (SR), the goal is not sparse representation, but sparse recovery. Furthermore try to make some modify on local window before perform PCA transform on it this modify include, change number of iteration according to the amount of noise on image additionally using the benefited of steering kernel regression (SKR) to prepare the noisy image before apply LPG-PCA. While kernel regression (KR) is a well studied method in statistics and signal processing, KR is identified as a nonparametric approach that requires minimal assumptions, and hence the framework is one of the appropriate approaches to the regression problem.
References
- Naveen Kulkarni, ' Compressive Sensing for Computer Vision and Image Processing', Approved May 2011.
- Jianchao Yang, Student Member, IEEE, John Wright, Member, IEEE, Thomas S. Huang, Fellow, IEEE, and Yi Ma, Senior Member, IEEE, 'Image Super-Resolution Via Sparse Representation' , IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 11, NOVEMBER 2010.
- H. Lee, A. Battle , R. Raina , A. Y. Ng, Efficient sparse coding algorithms, NIPS, 2007.
- Vikas D Patil ,'PCA Based Image Enhancement in Wavelet Domain', International Journal of Engineering Trends and Technology- Volume3Issue1- 2012.
- Lei Zhang, 'Two-stage image denoising by principal component analysis with local pixel grouping', Pattern Recognition 43 (2010) 1531–1549.
- M. Aharon, M. Elad, A. M. Bruckstein, The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation, IEEE Transaction on Signal Processing 54 (11) (2006) 4311–4322.
- M. Elad, M. Aharon, Image denoising via sparse and redundant representations over learned dictionaries, IEEE Transaction on Image Processing 15 (12) (2006) 3736–3745.
- A. Foi, V. Katkovnik, K. Egiazarian, Pointwise shape-adaptive DCT for high- quality denoising and deblocking of grayscale and color images, IEEE Transaction on Image Processing 16 (5) (2007).
- Jianchao Yang, Student Member, IEEE, John Wright, Member, IEEE, Thomas S. Huang, Fellow, IEEE, and Yi Ma, Senior Member, IEEE, 'Image Super-Resolution Via Sparse Representation' , IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 11, NOVEMBER 2010.
- L. C. Yang, L. Wright, T. S. Huang, and Y. Ma, "Image superresolution as sparse representation of raw image patches," Computer Vision and Pattern Recognition (CVPR 2008). IEEE Conference on. 2008.
- Charles-Alban Deledalle, 'Image denoising with patch based PCA: local versus global', c 2011.
- E. J. Candes, "Compressive Sampling," in Proc. Int. Congr. Mathetmaticians,2006, vol. 3, pp. 1433–1452.
- D. L. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006.
- D. Glasner, S. Bagon, and M. Irani. Super-resolution from a single image. In ICCV, 2009.
- Kaustubh Anil Patwardhan, A FEATURE-BASED ALGORITHM FOR SPIKE SORTING INVOLVING INTELLIGENT FEATURE-WEIGHTING MECHANISM, July 2011.
- W. T. Freeman, E. C, Pasztor, and O. T. Carmichael. "Learning Low Level Vision,", International Journal of Computer Vision, Vol. 40, 2000, pp. 25-47.
- Subhasis Chaudhuri, Manjunath V. Joshi. "Motin-Freesuper-Resolution", Springer Science Business Media, Inc, 2005.