Is large improvement in efficiency of impulsive noise removal in color images still possible?

Maliński, Łukasz; Radlak, Krystian; Smołka, Bogdan

doi:10.1371/journal.pone.0253117

Cited by 4 publications

(4 citation statements)

References 80 publications

(92 reference statements)

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“…The set of denoising algorithms chosen for the presentation of the proposed evaluation method contains: The vector median filter (VMF) [ 39 ]; The fast peer-group detector with the arithmetic mean filter corrector (FPG-AMF) [ 39 ]; The fast adaptive trimmed switching detector with the arithmetic mean filter corrector (FAST-AMF) [ 39 ]; The fast adaptive trimmed switching detector with the inpaint nans corrector (FAST-IPN) [ 39 , 47 ]. …”

Section: Resultsmentioning

confidence: 99%

“…The fast adaptive trimmed switching detector with the arithmetic mean filter corrector (FAST-AMF) [ 39 ];…”

Section: Resultsmentioning

confidence: 99%

“…Although a plethora of impulsive noise suppression algorithms have been proposed [ 28 , 29 , 30 , 31 , 32 ], a family of switching filters show very promising performance [ 33 , 34 , 35 , 36 , 37 , 38 , 39 ], which can only be outperformed by deep learning techniques [ 39 , 40 ]. These switching algorithms separate noise suppression into two steps: impulse detection and impulse correction [ 39 ]. In the first step, the pixels are categorized as noisy and noise-free.…”

Section: Introductionmentioning

confidence: 99%

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Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Maliński

2023

Sensors

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A new approach to the evaluation of digital image denoising algorithms is presented. In the proposed method, the mean absolute error (MAE) is decomposed into three components that reflect the different cases of denoising imperfections. Moreover, aim plots are described, which are designed to be a very clear and intuitive form of presentation of the new decomposed measure. Finally, examples of the application of the decomposed MAE and the aim plots in the evaluation of impulsive noise removal algorithms are presented. The decomposed MAE measure is a hybrid of the image dissimilarity measure and detection performance measures. It provides information about sources of errors such as pixel estimation errors, unnecessary altered pixels, or undetected and uncorrected distorted pixels. It measures the impact of these factors on the overall correction performance. The decomposed MAE is suitable for the evaluation of algorithms that perform a detection of the distortion that affects only a certain fraction of the image pixels.

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Section: Resultsmentioning

confidence: 99%

“…The fast adaptive trimmed switching detector with the arithmetic mean filter corrector (FAST-AMF) [ 39 ];…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Maliński

2023

Sensors

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“…The first one is the impulsive noise, which is caused by malfunctioning camera sensors, electromagnetic interference, aging of the storage material or transmission errors [1][2][3] . Basically, two models of impulsive noise are considered [4][5][6][7] . The first one is salt and pepper noise or fixed value impulse noise, in which the channel values of corrupted pixels are set to either the minimum or maximum of the dynamic range.…”

Section: Introductionmentioning

confidence: 99%

On the Reduction of Mixed Gaussian and Impulsive Noise in Heavily Corrupted Color Images

Smolka,

Kusnik,

Radlak

2023

Preprint

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In this paper, a novel approach to the mixed Gaussian and impulsive noise reduction in color images is proposed. The described denoising framework is based on the Non-Local Means (NLM) technique, which proved to efficiently suppress only the Gaussian noise. To circumvent the incapacity of the NLM filter to cope with impulsive distortions, a robust similarity measure between image patches, which is insensitive to the impact of impulsive corruption, was elaborated. To increase the effectiveness of the proposed approach, the blockwise NLM implementation was applied. However, instead of generating a stack of output images that are finally averaged, an aggregation strategy combining all weights assigned to pixels from the processing block was developed and proved to be more efficient. Based on the results of comparisons with the existing denoising schemes, it can be concluded that the novel filter yields satisfactory results when suppressing high-intensity mixed noise in color images. Using the proposed filter the image edges are well preserved and the details are retained while impulsive noise is efficiently removed. Additionally, the computational burden is not significantly increased, compared with the classic NLM, which makes the proposed modification applicative for practical image denoising tasks.

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On the reduction of mixed Gaussian and impulsive noise in heavily corrupted color images

Smolka,

Kusnik,

Radlak

2023

Sci Rep

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In this paper, a novel approach to the mixed Gaussian and impulsive noise reduction in color images is proposed. The described denoising framework is based on the Non-Local Means (NLM) technique, which proved to efficiently suppress only the Gaussian noise. To circumvent the incapacity of the NLM filter to cope with impulsive distortions, a robust similarity measure between image patches, which is insensitive to the impact of impulsive corruption, was elaborated. To increase the effectiveness of the proposed approach, the blockwise NLM implementation was applied. However, instead of generating a stack of output images that are finally averaged, an aggregation strategy combining all weights assigned to pixels from the processing block was developed and proved to be more efficient. Based on the results of comparisons with the existing denoising schemes, it can be concluded that the novel filter yields satisfactory results when suppressing high-intensity mixed noise in color images. Using the proposed filter the image edges are well preserved and the details are retained, while impulsive noise is efficiently removed. Additionally, the computational burden is not significantly increased, compared with the classic NLM, which makes the proposed modification applicative for practical image denoising tasks.

show abstract

Is large improvement in efficiency of impulsive noise removal in color images still possible?

Cited by 4 publications

References 80 publications

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

On the Reduction of Mixed Gaussian and Impulsive Noise in Heavily Corrupted Color Images

On the reduction of mixed Gaussian and impulsive noise in heavily corrupted color images

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