A survey of edge-preserving image denoising methods

Jain, Paras; Tyagi, Vipin

doi:10.1007/s10796-014-9527-0

Cited by 132 publications

(52 citation statements)

References 60 publications

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“…The precise estimation of is essential for the filtering quality, as well as for other image processing tasks such as the segmentation and the estimation of parameters to detect edges [24]. The LML approach has been proposed when the image information does not provide the information needed to detect edges and to determine the precise value of [25][26][27].…”

Section: Estimation Of Noise Standardmentioning

confidence: 99%

“…Previous to the information concealing, an adjustment to the escalation is done, based on the work [24]. This process is carried out in the ll1 to avoid that any inserted values in the IC visually alter the information and thus provoke changes in the resulting image.…”

mentioning

confidence: 99%

“…Adjust information of sub-band: An adjust operation is applied to the information in the recover sub-band = * √ 2 24 …”

mentioning

confidence: 99%

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Steganography Algorithm Based on the Nonlocal Maximum Likelihood Noise Estimation for Mobile Applications

Carvajal-Gámez

Díaz-Casco

2018

Security and Communication Networks

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In recent years, thanks to the use of Internet services, daily activities used to imply movement became more accessible to any user. As a result of such interconnection, now millions of people from different countries are able to communicate among themselves through the Internet, generating a great flow of data and classified information. The information on the Internet can be stolen, intercepted, anonymized, or even destroyed, resulting in cases of infringement of intellectual property rights, and the loss or damage of data. In such a globalized and interconnected world, solid security measures have become increasingly important to ensure data privacy protection and its confidentiality during transit. Nowadays, there is a variety of security mechanisms such as the steganography, an information hiding technique, which protects intellectual property by allowing the transmission of hidden data without drawing any suspicion. In order to achieve these criteria, an adaptation of the nonlocal maximum likelihood filter is proposed; in this class of filters, in general, they are used in images that require a high level of irregular pattern detection, based on the statistical dependence of the underlying pixels of the image analysis area, when using it in the wavelet domain as edge detector and/or discontinuities in images in order to have a greater selectivity when inserting information in the image. It strengthens the detection of the areas with the highest probability of having noise presenting results which are suitable areas to insert the information and that it is imperceptible in a quantitative and qualitative manner as presented in the Results and Discussion.

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Section: Estimation Of Noise Standardmentioning

confidence: 99%

mentioning

confidence: 99%

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Steganography Algorithm Based on the Nonlocal Maximum Likelihood Noise Estimation for Mobile Applications

Carvajal-Gámez

Díaz-Casco

2018

Security and Communication Networks

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“…It is well-known that the non-local mean (NLM) [13] algorithm leads to a better edge preservation [14], and this improvement is directly related to the diffusion process on the nonlinear geometric structure [15]. One can further consider the rotational structure of patch spaces to denoise images [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Manifold Learning via the Principle Bundle Approach

Lin

Minasian

et al. 2018

Front. Appl. Math. Stat.

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In this paper, we propose a novel principal bundle model and apply it to the image denoising problem. This model is based on the fact that the patch manifold admits canonical groups actions such as rotation. We introduce an image denoising algorithm, called the diffusive vector non-local Euclidean median (dvNLEM), by combining the traditional nonlocal Euclidean median (NLEM), the rotational structure in the patch space, and the diffusion distance. A theoretical analysis of dvNLEM, as well as the traditional nonlocal Euclidean median (NLEM), is provided to explain why these algorithms work. In particular, we show how accurate we could obtain the true neighbors associated with the rotationally invariant distance (RID) and Euclidean distance in the patch space when noise exists, and how we could apply the diffusion geometry to stabilize the selected metric. The dvNLEM is applied to an image database of 1,361 images and a comparison with the NLEM is provided. Different image quality assessments based on the error-sensitivity or the human visual system are applied to evaluate the performance.

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“…To handle the problems of linear filters, many non-linear edge-preserving methods have been developed and research is still continued in this direction [4]. These non-linear edgepreserving filtering methods can remove the noise more effectively while preserving the important image features such as edges.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Edge-Preserving Image Denoising using Arbitrarily Shaped Local Windows in Wavelet Domain

Jain¹,

Tyagi²

2015

IJCA

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Image denoising is a well explored topic in the field of image processing. A denoising algorithm is designed to suppress the noise while preserving as many image structures and details as possible. This paper presents a novel technique for edgepreserving image denoising using wavelet transforms. The multi-level decomposition of the noisy image is carried out to transform the data into the wavelet domain. An adaptive thresholding scheme which employs arbitrary shaped local windows and is based on edge strength is used to effectively reduce noise while preserving significant features of the original image. The experimental results, compared to other approaches, prove that the proposed method is suitable for various image types corrupted by Gaussian noise.

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A survey of edge-preserving image denoising methods

Cited by 132 publications

References 60 publications

Steganography Algorithm Based on the Nonlocal Maximum Likelihood Noise Estimation for Mobile Applications

Steganography Algorithm Based on the Nonlocal Maximum Likelihood Noise Estimation for Mobile Applications

Manifold Learning via the Principle Bundle Approach

Adaptive Edge-Preserving Image Denoising using Arbitrarily Shaped Local Windows in Wavelet Domain

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