Proceedings ArticleDOI
Extended Basis Pursuit Model and Its Application in Image De-noising
Wang Xiong-liang,Zhu Ju-bo,Wang Chun-Ling,Liang Dian-nong +3 more
- pp 295-299
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TLDR
In this paper, a new Extended Basis Pursuit De-Noising (EBPDN) model is brought forward and applied into salt-and-pepper noise removal, which can provide good de-noising results and outperforms other filters in terms of noise suppression and detail preservation.Abstract:
Traditional Basis Pursuit model is adapted to signal de-noising under additive Gaussian noise. Based on the different fitness error term, one new kind Extended Basis Pursuit De-Noising (EBPDN) model is brought forward and applied into salt-and-pepper noise removal. A comparison study of performance of the median filter, the peak-and-valley filter, the detail preserving filter and the EBPDN model is carried out using different types of images. EBPDN model can provide good de-noising results and outperforms other filters in terms of noise suppression and detail preservation.read more
Citations
More filters
Journal ArticleDOI
Distributed Basis Pursuit
TL;DR: The algorithm, named D-ADMM, is a decentralized implementation of the alternating direction method of multi- pliers, and it is shown through numerical simulation that the algorithm requires considerably less communications between the nodes than the state-of-the-art algorithms.
References
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Journal ArticleDOI
Atomic Decomposition by Basis Pursuit
TL;DR: Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions.
Journal ArticleDOI
Optimally sparse representation in general (nonorthogonal) dictionaries via 1 minimization
David L. Donoho,Michael Elad +1 more
TL;DR: This article obtains parallel results in a more general setting, where the dictionary D can arise from two or several bases, frames, or even less structured systems, and sketches three applications: separating linear features from planar ones in 3D data, noncooperative multiuser encoding, and identification of over-complete independent component models.
Journal ArticleDOI
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
TL;DR: This scheme can remove salt-and-pepper-noise with a noise level as high as 90% and show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only.
Journal ArticleDOI
Image decomposition via the combination of sparse representations and a variational approach
TL;DR: A novel method for separating images into texture and piecewise smooth (cartoon) parts, exploiting both the variational and the sparsity mechanisms is presented, combining the basis pursuit denoising (BPDN) algorithm and the total-variation (TV) regularization scheme.
Journal ArticleDOI
Detail preserving impulsive noise removal
TL;DR: A variation of the peak-and-valley filter based on a recursive minimum–maximum method, which replaces the noisy pixel with a value based on neighborhood information, which preserves constant and edge areas even under high impulsive noise probability.
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