Weighted laplacian differences based multispectral anisotropic diffusion

Prasath, V. B. Surya

doi:10.1109/igarss.2011.6050119

Cited by 6 publications

(4 citation statements)

References 14 publications

(20 reference statements)

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“…Compared with other schemes the splitting based scheme provides faster convergence as well as good restoration results. The scheme can be extended to handle multispectral images by using inter-channel correlations [37,33,35] and this defines our future work.…”

Section: Discussionmentioning

confidence: 99%

On convergent finite difference schemes for variational–PDE-based image processing

Prasath

Moreno

2017

Comp. Appl. Math.

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We study an adaptive anisotropic Huber functional based image restoration scheme. By using a combination of L2-L1 regularization functions, an adaptive Huber functional based energy minimization model provides denoising with edge preservation in noisy digital images. We study a convergent finite difference scheme based on continuous piecewise linear functions and use a variable splitting scheme, namely the Split Bregman [25], to obtain the discrete minimizer. Experimental results are given in image denoising and comparison with additive operator splitting, dual fixed point, and projected gradient schemes illustrate that the best convergence rates are obtained for our algorithm.

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Section: Discussionmentioning

confidence: 99%

On convergent finite difference schemes for variational–PDE-based image processing

Prasath

Moreno

2017

Comp. Appl. Math.

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“…[37], FISTA [10] or Chambolle-Pock first order primal-dual [21]. We consider here color image processing, that is, N = 3, for RGB channels and extension of the cross-channel term (6) to cases N > 3 is straightforward [51].…”

Section: Theorem 22 Given a Noise Image F And A Functionmentioning

confidence: 99%

“…We also consider the L 1 data fidelity term along with V T V regularization with coupling for image decomposition. The coupling terms provide better edge alignment and can also be used in a PDE formulation [55,51,53]. Here we utilize weighted T V regularization along with coupling gradient channels for obtaining decomposition and denoising of images.…”

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Color image processing by vectorial total variation with gradient channels coupling

Moreno¹,

Prasath²,

Neves³

2016

IPI

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We study a regularization method for color images based on the vectorial total variation approach along with channel coupling for color image processing, which facilitates the modeling of inter channel relations in multidimensional image data. We focus on penalizing channel gradient magnitude similarities by using L 2 differences, which allow us to explicitly couple all the channels along with a vectorial total variation regularization for edge preserving smoothing of multichannel images. By using matched gradients to align edges from different channels we obtain multichannel edge preserving smoothing and decomposition. A detailed mathematical analysis of the vectorial total variation with penalized gradient channels coupling is provided. We characterize some important properties of the minimizers of the model as well as provide geometrical results regarding the regularization parameter. We are interested in applying our model to color image processing and in particular to denoising and decomposition. A fast global minimization based on the dual formulation of the total variation is used and convergence of the iterative scheme is provided. Extensive experiments are given to show that our approach obtains good decomposition and denoising results in natural images. Comparison with previous color image decomposition and denoising methods demonstrate the advantages of our approach.

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Recent developments in computational color image denoising with PDEs to deep learning: a review

2021

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Weighted laplacian differences based multispectral anisotropic diffusion

Cited by 6 publications

References 14 publications

On convergent finite difference schemes for variational–PDE-based image processing

On convergent finite difference schemes for variational–PDE-based image processing

Color image processing by vectorial total variation with gradient channels coupling

Recent developments in computational color image denoising with PDEs to deep learning: a review

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