NDLI: A new efficient dictionary and its implementation on retinal images

Content Provider	IEEE Xplore Digital Library
Author	Thapa, D. Raahemifar, K. Lakshminarayanan, V.
Copyright Year	2014
Description	Author affiliation: Sch. of Optometry & Vision Sci., Univ. of Waterloo, Waterloo, ON, Canada (Thapa, D.; Lakshminarayanan, V.) \|\| Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada (Raahemifar, K.)
Abstract	Sparse representation of signals and images using an over-complete basis function (dictionary) has attracted a lot of attention in the literature recently. Atoms of a dictionary are either chosen from a predefined set of functions (e.g. Sine, Cosine or Wavelets), or learned from a training set (KSVD). Recently, a nonlinear (NL) dictionary has been proposed by adding NL functions, such as polynomials, rational, logarithmic, exponential, and phase shifted and higher order cosine functions to the conventional Discrete Cosine Transform (DCT) atoms. In this paper, we present a comprehensive performance comparison of various NL functions that are added to the DCT dictionary. The NL dictionary is also compared with the other known dictionaries such as DCT, Haar and KSVD-based learned dictionary for sparse image reconstruction. In the second part, the NL dictionary is exploited for sparsity based image denoising. Retinal images are used for the analysis.
Starting Page	841
Ending Page	846
File Size	1480995
Page Count	6
File Format	PDF
ISBN	9781479946129
ISSN	21653577
DOI	10.1109/ICDSP.2014.6900785
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Discrete cosine transforms Image reconstruction Polynomials Retina Digital signal processing PSNR OCT Denoising reconstruction dictionary sparsity nonlinear image processing ophthalmology retinal images
Content Type	Text
Resource Type	Article

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