A hypergraph-based algorithm for image restoration from salt and pepper noise

Dharmarajan, R.; Kannan, K.

doi:10.1016/j.aeue.2009.12.001

Cited by 29 publications

(12 citation statements)

References 22 publications

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“…The solution of (5) approaches that of (3) as → ∞ [7]. For practical implementation, as gradually increases, we use the previous solution as a "warm start" for the next alternating optimization.…”

Section: Weightedmentioning

confidence: 99%

“…In noise detection stage, noise candidates may be found in the spatial domain [2] or multiscale decomposition domain [3,4]. In the spatial domain, the size of local window is adaptively set [5,6] in a noise-aware way and a hypergraph can be defined as model relationship of a central pixel and its neighbor pixels [7]. In the multiscale decomposition domain, Wavelet transform has been adopted [3].…”

Section: Introductionmentioning

confidence: 99%

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Salt and Pepper Noise Removal with Noise Detection and a Patch-Based Sparse Representation

Guo

et al. 2014

Advances in Multimedia

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Images may be corrupted by salt and pepper impulse noise due to noisy sensors or channel transmission errors. A denoising method by detecting noise candidates and enforcing image sparsity with a patch-based sparse representation is proposed. First, noise candidates are detected and an initial guide image is obtained via an adaptive median filtering; second, a patch-based sparse representation is learnt from this guide image; third, a weighted 1 -1 regularization method is proposed to penalize the noise candidates heavier than the rest of pixels. An alternating direction minimization algorithm is derived to solve the regularization model. Experiments are conducted for 30%∼90% impulse noise levels, and the simulation results demonstrate that the proposed method outperforms total variation and Wavelet in terms of preserving edges and structural similarity to the noise-free images.

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Section: Weightedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Salt and Pepper Noise Removal with Noise Detection and a Patch-Based Sparse Representation

Guo

et al. 2014

Advances in Multimedia

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“…They are widely used in various fields such as computer science, game theory, data bases, data mining, optimization, image processing and segmentation [4,6,10].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Morphological filtering on hypergraphs

Vadakkenveettil

Unnikrishnan

Balakrishnan

et al. 2017

Discrete Applied Mathematics

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a b s t r a c tIn this work we study the framework of mathematical morphology on hypergraph spaces. Hypergraphs were introduced in the 60s as a natural generalization of graphs, where edges become hyperedges and can contain more than two vertices. Mathematical morphology is one of the most powerful frameworks for image processing, and is heavily used for many applications. However, morphological operators on hypergraph spaces is not a concept fully developed in the literature. We consider lattice structures on hypergraphs on which we build morphological operators. We propose several new openings, closings, granulometries and alternate sequential filters acting (i) on the subsets of the vertex and hyperedge set of a hypergraph and (ii) on the subhypergraphs of a hypergraph. We illustrate with applications in image processing for filtering objects defined on hypergraph spaces.

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“…We tend to improve Associate in Nursing existing distributed-coding algorithmic rule to search out sparse association between image patches. Dharmarajan and Kannan [5] have designed an algorithm for the hypergraph (HG) representation of an image, subsequent detection of Salt and Pepper (SP) noise in the image and finally the restoration of the image from this noise. The proposed algorithm exhibits superiority over traditional algorithms and recently proposed ones in terms of visual quality, Peak Signal to Noise Ratio (PSNR) and Mean Absolute Error (MAE).…”

Section: De Noising Techniquesmentioning

confidence: 99%