Sparse Technique for Images Corrupted by Mixed Gaussian-Impulsive Noise

Palacios-Enriquez, A.; Ponomaryov, Volodymyr; Reyes-Reyes, Rogelio; Sadovnychiy, Sergiy

doi:10.1007/s00034-018-0820-x

Cited by 8 publications

(6 citation statements)

References 41 publications

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“…To alleviate the above-mentioned problems, numerous image restoration approaches were designed to first remove impulsive noise and then, to apply an efficient filter intended for the reduction of the Gaussian noise component 26 – 30 . The rationale behind such an approach is that the nature of the two noise degradation types is distinct and they are easier to manage when considered separately.…”

Section: Related Workmentioning

confidence: 99%

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Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Kusnik

Smołka

2022

Sci Rep

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Noise reduction is one of the most important topics of digital image processing and despite the fact that it has been studied for a long time it remains the subject of active research. In the following work, we present an extension of the Mean Shift technique, which is efficiently reducing the Gaussian noise, so that it is able to cope with the impulsive disturbances. Furthermore, the elaborated technique can be applied to enhance the images corrupted by a mixture of strong Gaussian and impulsive noise, severely decreasing the quality of color digital images. By means of our approach, which is based on a novel similarity measure between a pixel and a patch located in the center of the processing block, even heavily disturbed images can be effectively restored, which enables the success of further stages of the image processing pipeline. We evaluate the efficiency of the proposed method using a publicly available database of test color images and compare the restored images applying a set of standard quality metrics with the results delivered by state-of-the-art denoising methods. Additionally, we compare our method with the Medoid and Quick Shift techniques, accelerating the original Mean Shift algorithm, in terms of objective quality criteria and computational complexity. The results of the performed experiments indicate that the proposed technique is superior to the widely used denoising techniques and can be used as a robust extension of the Mean Shift procedure. In the paper, a particular emphasis is placed on the ability of the presented algorithm to preserve and enhance image edges. The performed experiments evaluated with the use of the Pratt’s index, quantitatively confirm the superiority of the proposed design over the Mean Shift and standard denoising methods. The preservation of edges and even their sharpening is a very important feature of our algorithm whereas the final goal is segmentation, playing a crucial role in various computer vision tasks. The proposed algorithm is intended for the mixed noise reduction in color images, but it can be also applied in multispectral imaging and clustering of multidimensional data. To enable the comparison of our method with the standard denoising techniques and to help applying it in other image processing fields, we made its code freely available.

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Section: Related Workmentioning

confidence: 99%

“…The approach proposed in 30 consists of a fuzzy impulsive noise removal filter followed by additive noise reduction and a final postprocessing step. Once the impulsive noise is suppressed, a method based on sparse representation and 3D-processing performed with the use of DCT is applied.…”

Section: Related Workmentioning

confidence: 99%

Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Kusnik

Smołka

2022

Sci Rep

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“…However, demerits have been shown in the denoised images that obtained by these filters due to high level noise, distortions and blurred edges. In mixed noise type, there were some studies conducted [5][6][7][8][9]; the image quality has shown improvements compared to the techniques in classical filters up mentioned. On the other hand, some defects are shown up when it comes to the practical applications.…”

Section: Introductionmentioning

confidence: 99%

Natural Digital image Mixed Noise Removal Using Regularization Perona-Malik Model and Pulse Coupled Neural Networks

Khmag

2022

Preprint

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In the arena of digital image processing, mixed noise is normally shown. Nevertheless, the main limitation of the existing noise removal methods is the exploitation of the statistics of the original contaminated image only. This paper proposes a rapid and high accurate mixed noise removal method by combining Pulse Coupled Neural Networks (PCNN) and regularization of Perona-Malik equation (P-M equation) in order to remove unwanted contamination. In this regard, the locations of impulse noise are positioned by using PCNN, the second-generation wavelet filter is used in order to suppress the mixed noise into small local neighborhood, and then the full noisy image is denoised by exploiting the regularization of P-M equation. The fine details and sharp edges are well preserved in the proposed method. In addition, subjective and objective analyses are showed that the visual quality of the denoised technique outperformed state of the art noise removal methods. The experimental evaluation is conducted on well-known benchmark database images with mixed noise type. Furthermore, the results show that our proposed method can obtain more accurate and more reliable noise removal images than other approaches. Experimental results show that the proposed method provides accurate denoising performance with a low computational cost compared to nonlocal processing like NL-mean method.

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“…However, any kind of single filtering algorithm is difficult to obtain satisfactory effect because images tend to be polluted by two (or more) noise in the actual image processing. Some mixed noise filtering algorithms were proposed [7]- [11], Filtering performance of these algorithms has been improved compared with single algorithm, but they all have their own defects in the practical application. The median filtering was employed to process the impulse noise, and Gaussian noise was filtered by using the generalized variational model in the literature [7].…”

Section: Introductionmentioning

confidence: 99%

“…Some mixed noise filtering algorithms were proposed [7]- [11], Filtering performance of these algorithms has been improved compared with single algorithm, but they all have their own defects in the practical application. The median filtering was employed to process the impulse noise, and Gaussian noise was filtered by using the generalized variational model in the literature [7]. Literature [8] put forward weighted encoding with sparse nonlocal regularization for mixed noise removal, which performs soft impulse pixel detection via weighted encoding to deal with impulse noise and white Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

Mixed Image Noise Removal by PCNN Combined with Regularization P-M Equation

Liu,

ZHAO,

ZHAO

et al. 2022

Preprint

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In order to effectively remove the mixed noise, a novel algorithm for image reducing noise combined Pulse Coupled Neural Networks (PCNN) and regularization of Perona-Malik equation (P-M equation) is put forward. Firstly, the positions of impulse noise pollution are located by using PCNN, and the first stage adaptive noise filtering is applied according to the result of noise detection. Secondly, the multi-direction information median filtering is applied according to the local neighborhood noisy information. And then the Gaussian noise in the image is denoised by utilizing the regularization P-M diffusion equation. The theoretical analysis and experimental results show that the proposed algorithm needs no detection threshold, which has higher accuracy in the noise detection, is effectively able to remove the mixed noise, and can preserve the image details and the edges as well. The proposed algorithm has better subjective vision effect and objective quality than the wiener filter, median filter and other related algorithms. This algorithm put up better filter performance, high signal-to-noise ratio, denoising ability and strong adaptability.

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Sparse Technique for Images Corrupted by Mixed Gaussian-Impulsive Noise

Cited by 8 publications

References 41 publications

Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Natural Digital image Mixed Noise Removal Using Regularization Perona-Malik Model and Pulse Coupled Neural Networks

Mixed Image Noise Removal by PCNN Combined with Regularization P-M Equation

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