Impulse noise removal in highly corrupted color images

Cheikh, F.A.; Hamila, Ridha; Gabbouj, Moncef; Astola, Jaakko

doi:10.1109/icip.1996.559669

Cited by 23 publications

(11 citation statements)

References 5 publications

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“…In this example, the image ''Lena'' is blurred by a cross-channel blurring operator: 15) 0.15 · (G, 11,9) 0.15 · (G, 31, 13) 0.1 · (G, 21,11) 0 …”

Section: Example 42mentioning

confidence: 99%

“…A variety of techniques have been proposed for the removal of impulse noise, such as the vector median filter [5] and vector directional filter [6,7]. There are also methods for noise detection with the noise removal [8][9][10] which is out of scope here. Unfortunately, most of these filters were designed for denoising only and not suitable for deblurring [11].…”

Section: Introductionmentioning

confidence: 98%

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Restoration of blurred color images with impulse noise

Liu

Huang

et al. 2015

Computers & Mathematics with Applications

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a b s t r a c tRestoration of images degraded by blurring and impulse noise has received considerable attention recently. Guo et al. (2009) proposed a fast l 1 -total variation algorithm for grayscale image restoration with impulse noise. In this paper, we extend their idea for deblurring color images with impulse noise. An alternating iteration scheme is adopted for solving the corresponding problem. More importantly, we employ the five-point property to analyze the convergence of the proposed alternating algorithm. Numerical experiments demonstrate that the proposed method could deblur color images with good quality.

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“…In this example, the image ''Lena'' is blurred by a cross-channel blurring operator: 15) 0.15 · (G, 11,9) 0.15 · (G, 31, 13) 0.1 · (G, 21,11) 0 …”

Section: Example 42mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Restoration of blurred color images with impulse noise

Liu

Huang

et al. 2015

Computers & Mathematics with Applications

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“…This filter approach towards the contaminated or corrupted image is finding the noise using reduced ordering statics in conjunction with peer group filtering. The trimmed sum of distances is used by this filter and it is, k D; = Id;(r) (16) 1'=1 Where, k is the number of closer pixels taken for trimmed sum distance. For the pixel Xl assigns the value of D I .…”

Section: Fast Switching Filtermentioning

confidence: 99%

“…Noisy pixels are replaced by VMF and retain original pixels [17]. The technique which exploits detection estimation approaches which outliers first using adaptive threshold operator [16]. For de-noising color images adaptive mean and median hybrid filter was formulated to speed up the performance of the filter [13] [21].…”

Section: Introductionmentioning

confidence: 99%

An investigation on switching filters for impulse noise removal in color images

2015

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

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Color image processing is the field attracts more on the researchers in recent decades because it challenges with number of unsolvable problems. Especially, on the filtering techniques used to remove noises. Different noises incurred in an image and suppress or degrade the quality of an image and results make it poor. Impulsive noise is one among all noises and it should be prioritized as main, due to the damage happens on images are more on this type of noises. To enrich the properties of color image and also to gather original information for better compression and analysis, the impulsive noises should be taken apart from the color image by selecting appropriate better filters have been developed by researchers so far. Many switching filters have been designed for removal of these noises and that can be implemented for real time applications. This paper investigates some of the newly developed switching filters which uses the distance similarity measure and also analyze the computation methods.

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“…In addition, these methods may cause a smoothing out of the edges. The method proposed by Cheikh [4] first computes the energy in an n x n window and then uses this value to determine the extent of corruption of the center pixel. The corrupted pixel identification (CPI) method uses histogram distribution in each n x n window to determine the extent of corruption of the center pixel [14].…”

Section: Introductionmentioning

confidence: 99%

Impulsive noise filtering based on noise detection in corrupted digital color images

Sohn

Lee

Lim

2001

Circuits Systems and Signal Process

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This paper is an enhancement to our earlier research with grey-scale images. In this paper, we propose two new detection-estimation based image filtering algorithms that effectively remove corrupted pixels with impulsive noise in digital color images. The existing methods for enhancing corrupted color images typically possess inherent problems in computation time and smoothing out edges because all pixels are filtered. Our proposed algorithms first classify corrupted pixels in each channel or in each pixel. Because marginal or vector median filtering is only performed for the classified pixels, the process is computationally efficient, and edges are preserved well. In addition, because there is no appropriate criterion to evaluate the performance of impulsive noise detectors for color images, the objective comparison of noise detectors is difficult. Thus, we introduce a new efficiency factor for comparing the performance of noise detectors in digital color images. Simulation results show that the proposed algorithms perform better than existing methods, in both objective and subjective evaluations.

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Impulse noise removal in highly corrupted color images

Cited by 23 publications

References 5 publications

Restoration of blurred color images with impulse noise

Restoration of blurred color images with impulse noise

An investigation on switching filters for impulse noise removal in color images

Impulsive noise filtering based on noise detection in corrupted digital color images

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