Removal of salt-and-pepper noise based on compressed sensing

Huang, Shifeng; Zhu, Jiajian

doi:10.1049/el.2010.0833

Cited by 27 publications

(22 citation statements)

References 6 publications

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“…The proposed method is compared with AMF [2], sparsitybased denoising methods including TV [28,29] and dualtree complex Wavelet [12]. A suffix "ND" means that noise detection is applied in denoising.…”

Section: Resultsmentioning

confidence: 99%

“…Images are recovered much better than typical impulse denoising methods if appropriate sparsity priors are applied [12][13][14]. Beyond traditional multiscale decomposition methods, for example, Wavelet [3,4,12], sparse representations of image model wider image priors such as geometric directions [15][16][17] or redundancy [18] among images.…”

Section: Introductionmentioning

confidence: 99%

“…Beyond traditional multiscale decomposition methods, for example, Wavelet [3,4,12], sparse representations of image model wider image priors such as geometric directions [15][16][17] or redundancy [18] among images. In impulse noise removal, noise is detected by a sparse representation in identity matrix [13], or images are restored by enforcing their sparsity in Wavelet domain [12] or finite difference domain [19]. Rather than using fixed basis or dictionary, data-driven dictionaries have been proposed to provide an adaptive sparse representation for a specific image [20,21].…”

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: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…The two most common types of noise in image processing are Gaussian noise and Impulse noise, also known as salt and pepper noise. This type of noise may appear in digital image due contaminated impulse noise, which is caused by malfunctioning pixels in camera sensors, faulty memory location in hardware, or transmission in noisy channel [9]. Salt and pepper noise scattered throughout the image in such a way pixels can take only the maximum and minimum values (0 and 255 respectively) in the dynamic range.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Noise Reduction Scheme for Salt and Pepper

Gebreyohannes¹

2011

SIPIJ

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“…Finally, the sub-bands obtained from three methods are synthesised into the restored green channel. The peak signal-to-noise ratio (PSNR) value is used to evaluate these methods [7], and is defined by:…”

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confidence: 99%

Correlation of inter-channel high-frequency components in demosaicking weak correlated CFA data

et al. 2011

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A novel method for obtaining local high-frequency components of the missing colour channel in weak correlated colour filter array (CFA) data, by calculating the local inter-channel high-frequency correlation coefficients, is proposed. The contrast of experimental results demonstrates that the high-frequency components achieved by the proposed method are closer to the original image than those from state-of-theart approaches.Introduction: In a single chip sensor array, a colour filter array (CFA) is placed between the lance and the sensor for image acquisition. The CFA image is essentially a greyscale mosaic-pattern image as shown in Fig.1. The process for restoring the RGB full colour image from the CFA data is called colour interpolation or colour demosaicking.Gunturk et al. and Chen et al. have demonstrated that the highfrequency component of the green plane and that of the red/blue plane are highly correlated, the correlation values ranging from 0.98 to 1 [1, 2]. Most of the existing demosaicking algorithms replace the high-frequency components of the missing channel with those of the alternative pixel channel [1-4]. However, Zhang et al. and Chang and Tan have found that the assumption of high-frequency correlation does not hold in areas of highly saturated colours (weak correlated image) [5,6]. Zhang supplements an existing colour demosaicking algorithm, which exploits the same inter-channel high-frequency correlation as methods in [1][2][3][4], by combining its results with linear minimum mean-square estimation and the support vector regression. Chang utilises the spatial and spectral correlations to interpolate the CFA image. Their methods perform better than previous algorithms, especially when restoring the weak correlated CFA image.This Letter focuses on the local relationship of inter-channel highfrequency components, instead of looking at it as one global problem as previous methods, when interpolating the weak correlated CFA image. The contribution of this Letter is acquiring the locally varied high-frequency components of the weak correlated CFA image, according to the local inter-channel high-frequency correlation coefficients in the wavelet domain. Experimental results show that the proposed method outperforms or performs close to the state-of-the-art approaches.

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Removal of salt-and-pepper noise based on compressed sensing

Cited by 27 publications

References 6 publications

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

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

Adaptive Noise Reduction Scheme for Salt and Pepper

Correlation of inter-channel high-frequency components in demosaicking weak correlated CFA data

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