Image Denoising Using Sparse Representations

Valiollahzadeh, SeyyedMajid; Firouzi, Hamed; Babaie‐Zadeh, Massoud; Jutten, Christian

doi:10.1007/978-3-642-00599-2_70

Cited by 14 publications

(7 citation statements)

References 13 publications

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“…So a thought is put forward based on the former researchers [17] that dividing the image into blocks and suppressing noise by iterative residuals.…”

Section: Algorithm Of Noise Depression In Image Based On Learningmentioning

confidence: 99%

Denoising star map data via sparse representation and dictionary learning

Zhou

Shi

Yang

2015

Optik

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“…So a thought is put forward based on the former researchers [17] that dividing the image into blocks and suppressing noise by iterative residuals.…”

Section: Algorithm Of Noise Depression In Image Based On Learningmentioning

confidence: 99%

Denoising star map data via sparse representation and dictionary learning

Zhou

Shi

Yang

2015

Optik

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“…Sparse transforms have been found to be among the most successful methods for denoising [27], and dictionary learning methods have been used for this application [13]. Table III shows the tolerance of the PCA, K-SVD and GAD algorithms to a noise level changing from 20 dB to -10 dB, as the number of atoms in the reconstruction is reduced from 512 to 50.…”

Section: Application To Speech Denoisingmentioning

confidence: 99%

Fast Dictionary Learning for Sparse Representations of Speech Signals

Jafari

Plumbley

2011

IEEE J. Sel. Top. Signal Process.

113

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For dictionary-based decompositions of certain types, it has been observed that there might be a link between sparsity in the dictionary and sparsity in the decomposition. Sparsity in the dictionary has also been associated with the derivation of fast and efficient dictionary learning algorithms. Therefore, in this paper we present a greedy adaptive dictionary learning algorithm that sets out to find sparse atoms for speech signals. The algorithm learns the dictionary atoms on data frames taken from a speech signal. It iteratively extracts the data frame with minimum sparsity index, and adds this to the dictionary matrix.The contribution of this atom to the data frames is then removed, and the process is repeated. The algorithm is found to yield a sparse signal decomposition, supporting the hypothesis of a link between sparsity in the decomposition and dictionary.The algorithm is applied to the problem of speech representation and speech denoising, and its performance is compared to other existing methods. The method is shown to find dictionary atoms that are sparser than their time-domain waveform, and also to result in a sparser speech representation. In the presence of noise, the algorithm is found to have similar performance to the well established principal component analysis. Index TermsSparse decomposition, adaptive dictionary, sparse dictionary, dictionary learning, speech analysis, speech denoising.

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“…This allows to better match natural image content by choosing the transform to strengthen the sparsity assumption, which is at the heart of these methods. The algorithm we use to solve this optimization is basically on the basis of block-coordinate-relaxation method [8,12] taking the advantage of ¼ -norm solution [13]. The overall algorithm is as follows: similar to block-coordinate descent [12], one image (say c ½ ) is fixed while the other, c ¾ is updated and vice-versa.…”

Section: Modeling Inpainting and The Final Proposed Algorithmmentioning

confidence: 99%

“…In other words it is the MAP estimation of the true coefficient with this prior that we have the most possible sparsity in representation. Figure 1 states a procedure for solution of such a problem [13]. Once the coefficients estimated we can reconstruct the c ½ by A «.…”

Section: Modeling Inpainting and The Final Proposed Algorithmmentioning

confidence: 99%