Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary. Aharon, M. & Elad, M. SIAM Journal on Imaging Sciences, 1(3):228–247, January, 2008. Publisher: Society for Industrial and Applied Mathematics TLDR: This paper proposes a novel structure of a model for representing image content by replacing a probabilistic averaging of patches with their sparse representations, and presents high-quality image denoising results based on this new model.![Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper presents a framework for learning multiscale sparse representations of color images and video with overcomplete dictionaries. A single-scale K-SVD algorithm was introduced in [M. Aharon, M. Elad, and A. M. Bruckstein, IEEE Trans. Signal Process., 54 (2006), pp. 4311–4322], formulating sparse dictionary learning for grayscale image representation as an optimization problem, efficiently solved via orthogonal matching pursuit (OMP) and singular value decomposition (SVD). Following this work, we propose a multiscale learned representation, obtained by using an efficient quadtree decomposition of the learned dictionary and overlapping image patches. The proposed framework provides an alternative to predefined dictionaries such as wavelets and is shown to lead to state-of-the-art results in a number of image and video enhancement and restoration applications. This paper describes the proposed framework and accompanies it by numerous examples demonstrating its strength.
@article{aharon_sparse_2008,
title = {Sparse and {Redundant} {Modeling} of {Image} {Content} {Using} an {Image}-{Signature}-{Dictionary}},
volume = {1},
url = {https://epubs.siam.org/doi/10.1137/07070156X},
doi = {10.1137/07070156X},
abstract = {This paper presents a framework for learning multiscale sparse representations of color images and video with overcomplete dictionaries. A single-scale K-SVD algorithm was introduced in [M. Aharon, M. Elad, and A. M. Bruckstein, IEEE Trans. Signal Process., 54 (2006), pp. 4311–4322], formulating sparse dictionary learning for grayscale image representation as an optimization problem, efficiently solved via orthogonal matching pursuit (OMP) and singular value decomposition (SVD). Following this work, we propose a multiscale learned representation, obtained by using an efficient quadtree decomposition of the learned dictionary and overlapping image patches. The proposed framework provides an alternative to predefined dictionaries such as wavelets and is shown to lead to state-of-the-art results in a number of image and video enhancement and restoration applications. This paper describes the proposed framework and accompanies it by numerous examples demonstrating its strength.},
language = {en},
number = {3},
urldate = {2023-08-04},
journal = {SIAM Journal on Imaging Sciences},
author = {Aharon, Michal and Elad, Michael},
month = jan,
year = {2008},
note = {Publisher: Society for Industrial and Applied Mathematics
TLDR: This paper proposes a novel structure of a model for representing image content by replacing a probabilistic averaging of patches with their sparse representations, and presents high-quality image denoising results based on this new model.},
keywords = {\#Representation, \#Sparse, \#Vision, /unread},
pages = {228--247},
}
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