A mixed noise removal algorithm based on multi-fidelity modeling with nonsmooth and nonconvex regularization

Li, Chun; Li, Yuepeng; Zhao, Zhicheng; Yu, Longlong; Luo, Ze

doi:10.1007/s11042-019-7625-1

Cited by 8 publications

(7 citation statements)

References 68 publications

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“…The partition-based methods for noise suppression in color digital images [21,22] arrange the pixels in different signal activity classes, which are associated to adequate denoising methods. Another important class of filters are the regularization methods [23][24][25][26] founded on partial differential equations. These methods have been employed for color [23,24] and grayscale images [25].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

Arnal

Súcar

2019

Applied Sciences

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To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of impulse noise at the first stage. At the second stage, to remove Gaussian noise, a fuzzy peer group method is applied on the image generated from the previous stage. The performance of the introduced algorithm was evaluated on standard test images employing widely used objective quality metrics. The new approach can efficiently reduce both impulse and Gaussian noise, as much as mixed noise. The proposed filtering method was compared to the state-of-the-art methodologies: adaptive nearest neighbor filter, alternating projections filter, color block-matching 3D filter, fuzzy peer group averaging filter, partition-based trimmed vector median filter, trilateral filter, fuzzy wavelet shrinkage denoising filter, graph regularization filter, iterative peer group switching vector filter, peer group method, and the fuzzy vector median method. The experiments demonstrated that the introduced noise reduction technique outperforms those state-of-the-art filters with respect to the metrics peak signal to noise ratio (PSNR), the mean absolute error (MAE), and the normalized color difference (NCD).

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Section: Introductionmentioning

confidence: 99%

Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

Arnal

Súcar

2019

Applied Sciences

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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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“…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. A weighted averaging method of fuzzy rule system was proposed to reduce Gaussian and impulsive noise from color images in the literature [10].…”

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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A mixed noise removal algorithm based on multi-fidelity modeling with nonsmooth and nonconvex regularization

Cited by 8 publications

References 68 publications

Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color 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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