Nonconvex sparse regularizer based speckle noise removal

Han, Yu; Feng, Xiaoyi; Baciu, George; Wang, Weiwei

doi:10.1016/j.patcog.2012.10.010

Cited by 54 publications

(60 citation statements)

References 30 publications

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“…A nonconvex TGV regularizer is used to make the whole regions of the image efficiently smoothed while it is used to make the edges well preserved. As demonstrated in our numerical experiments, the nonconvex TGV-based despeckling method not only outperforms the TGV methods (TGVSNR algorithm) [13] by better preserving edges of images but also is far better than the nonconvex TV-based method (NRSNR algorithm) [17] by removing the staircasing artifacts.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 94%

“…Originally, the iteratively reweighted algorithm proceeds by iteratively solving 1 problems which approximate the original problem [17,20,21], which was proposed to improve the sparsity in 1 regularized compressed sensing problems, but it turns out that this algorithm is also useful for computer vision applications.…”

Section: The Iteratively Reweighted Methodmentioning

confidence: 99%

“…Nikolova et al [14] discuss the properties of the nonconvex regularizer for removing additive noise and then [15,16] study ℓ 1 data fitting and concave regularization for image recovery problem. Han et al [17] apply nonconvex TV regularizer to the speckle noise removal, which better preserves edges of restored images compared to classical TV regularizer-based methods. Ochs et al [18] combine nonconvexity with total generalized variation for reducing the additive noise, deconvolution, and some other applications.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

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Speckle Noise Reduction via Nonconvex High Total Variation Approach

Feng

2015

Mathematical Problems in Engineering

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We address the problem of speckle noise removal. The classical total variation is extensively used in this field to solve such problem, but this method suffers from the staircase-like artifacts and the loss of image details. In order to resolve these problems, a nonconvex total generalized variation (TGV) regularization is used to preserve both edges and details of the images. The TGV regularization which is able to remove the staircase effect has strong theoretical guarantee by means of its high order smooth feature. Our method combines the merits of both the TGV method and the nonconvex variational method and avoids their main drawbacks. Furthermore, we develop an efficient algorithm for solving the nonconvex TGV-based optimization problem. We experimentally demonstrate the excellent performance of the technique, both visually and quantitatively.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 94%

Section: The Iteratively Reweighted Methodmentioning

confidence: 99%

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

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Speckle Noise Reduction via Nonconvex High Total Variation Approach

Feng

2015

Mathematical Problems in Engineering

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“…Speckle noise is a specific noise occurring in coherent light imaging which is signal dependent [24]. It is a non-Gaussian kind of noise and consequently becomes one of the complex noise models.…”

Section: Speckle Noisementioning

confidence: 99%

“…It is a non-Gaussian kind of noise and consequently becomes one of the complex noise models. Since coherent light imaging is used in laser and radar imaging, speckle noise is an inherent property of radar images and thus it has a specified area of interest in noise analysis [24]. Contrary to the most common Gaussian noise, speckle noise is multiplicative in nature and given for any image I as I s =I+N×I where I s is speckled image N is uniform noise characterized by the PDF [21,22] …”

Section: Speckle Noisementioning

confidence: 99%

Effect of Noise in Estimation of Fractal Dimension of Digital Images

Pant

2013

IJSIP

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Active contours driven by normalized local image fitting energy

Peng

Liu

2013

Concurrency and Computation

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SUMMARY In this paper, a new local region‐based active contour model that is driven by normalized local image fitting energy is proposed. By considering the local image fitting energy to extract the local image information, our model can effectively and efficiently segment images with intensity inhomogeneities. In addition, to keep the smoothness of the level set function, the time‐consuming reinitialization step widely adopted in traditional level set methods can be avoided by introducing a Gaussian filtering process. Experimental results on synthetic and real images demonstrate that the proposed model has much less CPU time and sensitivity to the initial contour than the well‐known local binary fitting model. Copyright © 2013 John Wiley & Sons, Ltd.

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Nonconvex sparse regularizer based speckle noise removal

Cited by 54 publications

References 30 publications

Speckle Noise Reduction via Nonconvex High Total Variation Approach

Speckle Noise Reduction via Nonconvex High Total Variation Approach

Effect of Noise in Estimation of Fractal Dimension of Digital Images

Active contours driven by normalized local image fitting energy

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