Two-stage quaternion switching vector filter for color impulse noise removal

Jin, Lianghai; Zhu, Zhiliang; Xu, Xiangyang; Li, Xiang

doi:10.1016/j.sigpro.2016.03.025

Cited by 31 publications

(33 citation statements)

References 43 publications

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“…Conversely, computational complexity must be maintained at a low level for the preservation of edges and removal of impulse noise. A two-stage quaternion switching filter [10] was proposed to remove the impulse noise in the color images. In this method, an effective color distance estimation method was proposed by using quaternion representation.…”

Section: Literature Surveymentioning

confidence: 99%

A sporadic decomposition of Hankel structured matrix in logarithmic and wavelet domain for impulse noise removal

Victoria.L¹,

Sathappan

2018

IJET

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Noise removal from the color images is the most significant and challenging task in image processing. Among different conventional filter methods, a robust Annihilating filter-based Low-rank Hankel matrix (r-ALOHA) approach was proposed as an impulse noise removal algorithm that uses the sparse and low-rank decomposition of a Hankel structured matrix to decompose the sparse impulse noise components from an original image. However, in this algorithm, the patch image was considered as it was sparse in the Fourier domain only. It requires an analysis of noise removal performance by considering the other transform domains. Hence in this article, the r-ALOHA can be extended into other transform domains such as log and exponential. In the log and exponential domain, the logarithmic and exponential functions are used for modeling the multiplicative noise model. But, this model is used only for positive outcomes. Therefore, wavelet transform domain is applied to the noise model that localizes an image pixel in both frequency and time domain simultaneously. Moreover, it separates the most vital information in a given image. Thus, it is feasible for obtaining a better approximation of the considered function using few coefficients. Finally, the experimental results show the performance effectiveness of the proposed algorithm.

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Section: Literature Surveymentioning

confidence: 99%

A sporadic decomposition of Hankel structured matrix in logarithmic and wavelet domain for impulse noise removal

Victoria.L¹,

Sathappan

2018

IJET

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“…Ananthi and Balasubramaniam [8] use the technique of the interval-valued fuzzy set. Jin et al [9] employ the quaternion theory. Malinski and Smolka [10] use adaptive switching technique.…”

Section: Introductionmentioning

confidence: 99%

Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images

2017

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Impulsive noise removal for color images usually employs vector median filter, switching median filter, the total variation 1 method, and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A marginal method to reduce impulsive noise is proposed in this paper that overcomes this limitation that is based on the following facts: (i) each channel in a color image is contaminated independently, and contaminative components are independent and identically distributed; (ii) in a natural image the gradients of different components of a pixel are similar to one another. This method divides components into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the components are divided into the corrupted and the noise-free components; if the image is corrupted by random-valued impulses, the components are divided into the corrupted, noise-free, and the possibly corrupted components. Components falling into different categories are processed differently. If a component is corrupted, modified total variation diffusion is applied; if it is possibly corrupted, scaled total variation diffusion is applied; otherwise, the component is left unchanged. Simulation results demonstrate its effectiveness.

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“…Jin et al [9] proposed a solution that uses quaternion rotation operations to compute color differences. Similar methods, such as quaternion switching VMF (QSVMF) [9], quaternion directional switching VMF (QDSVMF) [10], and two-stage quaternion switching vector filter (TQSVF) [11] measure differences in color based on Euclidean distance. Unfortunately, these methods commonly employ complex transformations.…”

Section: Introductionmentioning

confidence: 99%

“…The quality of reconstruction was measured using peak-to-signal ratio (PSNR), mean absolute error (MAE) and the normalized color difference (NCD) [11]. Figure 1 illustrates the visual quality of filtered images and noisy images with noise ratios from 5% to 25%.…”

mentioning

confidence: 99%

“…The performance of the proposed algorithm was also compared with other impulse noise filters: classical vector filters such as vector median filter (VMF) [1], basic vector directional filter (BVDF) [2], and directional-distance filter (DDF) [3], and other well-known vector filters such as adaptive vector median filter (AVMF) [4], robust switching VMF (RSVMF) [5], edge detection based switching VMF (EDSVMF) [6], fast peer group filter for VMF (FPGFvmf) [7], fast averaging peer group filter (FAPGF) [8], quaternion switching VMF(QSVMF) [9], quaternion directional switching VMF (QDSVMF) [10], and two-stage quaternion switching vector filter (TQSVF) [11]. Table I lists result of the performance comparison in terms of PSNR, MAE, and NCD.…”

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confidence: 99%

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Directional 3D Peer-Group Filter for Color Image with Random Impulse Noise

Hsu¹,

Chou²,

Lee³

2017

dtcse

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Abstract. Peer-group filtering is the most basic approach to image denoising in the spatial domain; however, the effectiveness of this method degrades rapidly with an increase in the amount of noise. In this paper, we propose a novel 3D (3 dimensions) directional peer-group filter (referred to as 3DPGF) for the restoration of color images through the removal of random impulse noise. As with other switching methods, 3DPGF proceeds through two steps. Noise detection stage is first performed using a directional peer-group method, whereupon a 3D peer-group weighted-mean technique is used to remove the noise. Simulation results demonstrate that 3DPGF is able to enhance the accuracy in the identification and removal of noise.

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Two-stage quaternion switching vector filter for color impulse noise removal

Cited by 31 publications

References 43 publications

A sporadic decomposition of Hankel structured matrix in logarithmic and wavelet domain for impulse noise removal

A sporadic decomposition of Hankel structured matrix in logarithmic and wavelet domain for impulse noise removal

Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images

Directional 3D Peer-Group Filter for Color Image with Random Impulse Noise

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