Open Access
Subscription Access
Open Access
Subscription Access
An Approach to Study Image Denoising using Doubly Sparse Transform Technique
Subscribe/Renew Journal
In this Paper the sparse domain of signals in a certain area or dictionary has been widely used in many applications in image, audio, biological and other signal analysis. Analytical Sparse transforms such as discrete cosine transform (DCT) and its counterpart i.e. wavelet transform (WT) have been extensively used in the areas of image compression standards where as synthesis sparsifying dictionaries have become extensively used especially in applications such as image de-noising and medical image reconstruction. In this work, we discuss about the square sparsifying transforms which is the product from a fixed, fast transform so as to consider the DCT and an adaptive constrained matrix to be sparse. Such transforms can be studied and implemented efficiently.
Keywords
Dictionary Learning, Sparse Representation, Image De-Noising, Wavelet Transform, Discrete Cosine Transform.
User
Subscription
Login to verify subscription
Font Size
Information
- S. Ravishankar, and Y. Bresler, “Learning Doubly Sparse Transforms for Images,” IEEE Transactions on Signal Processing, vol. 22, no. 12, pp. 4598-4612, Dec. 2013.
- R. Rubinstein, M. Zibulevsky, and M. Elad, “Double sparsity: Learning sparse dictionaries for sparse signal approximation,” IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1553–1564, Mar. 2010.
- M. Yaghoobi, S. Nam, R. Gribonval, and M. E. Davies, “Constrained overcomplete analysis operator learning for cosparse signal modelling,” IEEE Transactions on Signal Processing, vol. 61, no. 9, pp. 2341–2355, May 2013.
- S. Ravishankar and Y. Bresler, “Learning sparsifying transforms,” IEEE Transactions on Signal Processing, vol. 61, no. 5, pp. 1072–1086, Mar. 2013.
- M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Transactions on Signal Processing, vol. 15, no. 12, pp. 3736–3745, Dec. 2006.
- M. Aharon, M. Elad, and A. M. Bruckstein, “The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311-4322, Nov. 2006
- Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu, “Automatic single-image-based rain streaks removal via image decomposition,” IEEE Transactions on Signal Processing, vol. 21, no. 4, pp. 1742-1755, APRIL. 2012.
- S. Mallat, and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3397-3415, Dec. 1993.
- J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” Journal of Machine Learning Research, vol. 11, pp. 19-60, Jan. 2010.
Abstract Views: 369
PDF Views: 0