Image Deblurring – A Case Study
In this chapter we present an application of the Sparse-Land model to image deblurring, in order to demonstrate the applicative side of the above-discussed model and algorithms. As we show next, this long-studied problem can be handled quite effectively using the fundamentals of the model with hardly any changes. The content of this chapter follows closely with the work by M.A.T. Figueiredo and R.D. Nowak that appeared in ICIP 2005, and a later paper by M. Elad, B. Matalon, and M. Zibulevsky (2007). While there exists a more recent work that leads to somewhat improved results, the appeal in this work is the relative simplicity with which near-state-of-the-art results are obtained.
KeywordsNoisy Image Haar Wavelet Blur Kernel Image Deconvolution Image Deblurring
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- 2.K. Dabov, A. Foi, and K. Egiazarian, Image restoration by sparse 3D transformdomain collaborative filtering, Proc. SPIE Electronic Imaging ’08, no. 6812–07, San Jose, California, USA, January 2008.Google Scholar
- 5.M. Elad, B. Matalon, J. Shtok, and M. Zibulevsky, A wide-angle view at iterated shrinkage algorithms, SPIE (Wavelet XII) 2007, San-Diego CA, August 26–29, 2007.Google Scholar
- 6.M.J. Fadili, J.-L. Starck, Sparse representation-based image deconvolution by iterative thresholding, Astronomical Data Analysis ADA’06, Marseille, France, September, 2006.Google Scholar
- 8.M.A. Figueiredo, and R.D. Nowak, A bound optimization approach to waveletbased image deconvolution, IEEE International Conference on Image Processing - ICIP 2005, Genoa, Italy, 2:782–785, September 2005.Google Scholar