Block-based noise estimation using adaptive gaussian filtering

Shin, Donghyuk; Park, Rae-Hong; Yang, Seungjoon; Jung, Joël

doi:10.1109/tce.2005.1405723

Cited by 168 publications

(17 citation statements)

References 14 publications

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“…Taking as a starting point the sub-optimal values obtained in [34], we analyzed the adjustment of the parameters th 1 , th 2 , and th 3 in the hybrid structure in presence of mixed noise. By means of extensive experiments, we have observed that sub-optimal performance in terms of PSNR can be obtained for th 1 , th 2 , and th 3 as a function of the amount of noise σ and p. The results reveal that for p ∈ [0, 0.4], and σ ∈ [0, 40], we can achieve sub-optimal performance by setting th 1 , th 2 , and th 3 as a function of p and σ as follows,…”

Section: Resultsmentioning

confidence: 98%

“…where β p,σ = p − 2σ/100 depends on the impulsive noise p, and σ, the standard deviation for the Gaussian noise. There exist different techniques to estimate σ and p; e.g., p can be estimated with the technique used in [39] and σ can be estimated by the approach introduced in [40]. Then, using estimations of σ and p, the parameters th 1 , th 2 , and th 3 can be automatically adjusted.…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

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

Arnal

Súcar

2019

Applied Sciences

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“…where y is the observed noisy image, x is the unknown clean image, and n represents additive white Gaussian noise (AWGN) with standard deviation σ n , which can be estimated in practical applications by various methods, such as median absolute deviation [5], block-based estimation [6], and principle component analysis (PCA)based methods [7]. The purpose of noise reduction is to decrease the noise in natural images while minimizing the loss of original features and improving the signal-to-noise ratio (SNR).…”

Section: Image Denoising Problem Statementmentioning

confidence: 99%

Brief review of image denoising techniques

Fan

Zhang²,

Fan³

et al. 2019

Vis. Comput. Ind. Biomed. Art

345

176

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With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.

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“…After determining the ROI, the procedures related to the process of gas nitriding zones' identification, processes associated with improving the image quality are performed, which include, depending on the method of sample preparation and core material parameters: Scaling in two-dimensional space,Contrast enhancement using one of the functions of the type: 1 − e − C , ln(1 + C ), tanh( C ), where: C —former contract value,Filtration, which aims to compensate the imperfections of the image acquisition process or ineffective exposure of the tested object (filters: median [18]; Gauss [19]).…”

Section: Model Of the Processes Of The Manufacture Of Nitrided Laymentioning

confidence: 99%

“…Filtration, which aims to compensate the imperfections of the image acquisition process or ineffective exposure of the tested object (filters: median [18]; Gauss [19]).…”

Section: Model Of the Processes Of The Manufacture Of Nitrided Laymentioning

confidence: 99%

Heuristic Analysis Model of Nitrided Layers’ Formation Consisting of the Image Processing and Analysis and Elements of Artificial Intelligence

Wójcicki

Nowicki

2016

Materials

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The article presents a selected area of research and development concerning the methods of material analysis based on the automatic image recognition of the investigated metallographic sections. The objectives of the analyses of the materials for gas nitriding technology are described. The methods of the preparation of nitrided layers, the steps of the process and the construction and operation of devices for gas nitriding are given. We discuss the possibility of using the methods of digital images processing in the analysis of the materials, as well as their essential task groups: improving the quality of the images, segmentation, morphological transformations and image recognition. The developed analysis model of the nitrided layers formation, covering image processing and analysis techniques, as well as selected methods of artificial intelligence are presented. The model is divided into stages, which are formalized in order to better reproduce their actions. The validation of the presented method is performed. The advantages and limitations of the developed solution, as well as the possibilities of its practical use, are listed.

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Block-based noise estimation using adaptive gaussian filtering

Cited by 168 publications

References 14 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

Brief review of image denoising techniques

Heuristic Analysis Model of Nitrided Layers’ Formation Consisting of the Image Processing and Analysis and Elements of Artificial Intelligence

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