Overview of the Applications of Curvelet Transform in Image Processing

Gang, Long Hui; Xiao, Lei; Xuequan, Chen

doi:10.1360/crad20050808

Cited by 18 publications

(8 citation statements)

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“…Botnet means a computer group that can be controlled by bot-master remotely through a command and control channel (Fang, et al, 2011). The bot-master refers to the attacker who has ability to control the botnet and make it into some particular states.…”

Section: Background Definition Of Mobile Botnetmentioning

confidence: 99%

Design of Mobile Botnet Based on Open Service

Sun

Zhai

et al. 2016

International Journal of Digital Crime and Forensics

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In recent years, botnet has become one of the most serious security threats to Internet. With the rapid development of mobile network and the popularity of smartphones, botnet began to spread to mobile platform. In order to counter mobile botnet, it is meaningful to study its constructive mechanism and reproduce it. In the past studies, researchers have designed several kinds of mobile botnet model based on various communication channels, such as SMS, Bluetooth, etc. This paper proposed a general mobile botnet model based on open service, and verified its feasibility by implementing it on Android platform. This paper also analyzed this model, and then proposed potential defense strategy in terms of its characteristic, which may provide reliable theoretical and technical support for future prevention and privacy protection.

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Section: Background Definition Of Mobile Botnetmentioning

confidence: 99%

Design of Mobile Botnet Based on Open Service

Sun

Zhai

et al. 2016

International Journal of Digital Crime and Forensics

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“…Discrete curvelet transforms provide near-ideal sparsity of representation for both smooth objects and objects with edges. Curvelet denoising will obtain dramatically smaller asymptotic mean square error of reconstruction than wavelet methods, in recovering images which are smooth away from edges [18][19][20].…”

Section: Preserving Edges Using Curveletmentioning

confidence: 99%

Improving performance of K-SVD based image denoising using curvelet transform

Routray

Ray

Mishra

2015

2015 International Conference on Microwave, Optical and Communication Engineering (ICMOCE)

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Image denoising algorithm in transform domain which uses learning of dictionary has better PSNR performance than others. It is seen that the popular algorithms based on K-SVD proposed earlier has still in use. However, the texture part of the image could not be preserved during the process of denoising. It is also seen that the effect becomes more visible with increased value of standard deviation of the gaussian noise. The proposed algorithm in this work uses curvelet transform along with K-SVD to retain the texture part of the image. The denoising with the proposed method shows better PSNR performance as compared to denoising with only K-SVD.

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“…where WA denotes the transform of wave atoms and WA-1 is its inverse transform and more information about wave atoms also see [10][11][12], while P is curvelets shrinkage which is described in [9][10][11].…”

Section: Why Use a Hybrid Diffusivity Term?mentioning

confidence: 99%

“…In [9][10][11], Duan etc introduced special models which can reach better nonlinear approximation to edge by curvelets. But it is not an optimal choose for textures.…”

Section: Introductionmentioning

confidence: 99%

A well-balanced reaction-diffusion model for texture image denoising

Gong

2013

SPIE Proceedings

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Important information, such as textures or edge, is often lost or blurred in the process of traditional denoising. In this paper we present a well balanced reaction-diffusion equation combined new harmonic analysis to resolve this problem better. A mixed diffusivity term is used to control the diffusion process to smooth and maintain essential image characteristics. While in reaction terms we employ wave atoms and curvelets shrinkage to preserve and enhance textures and edges. Experiment results and comparisons show the good performance of the proposed model for texture and edge preserving in the process of denoising especially as the noise increase.

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Overview of the Applications of Curvelet Transform in Image Processing

Cited by 18 publications

References 0 publications

Design of Mobile Botnet Based on Open Service

Design of Mobile Botnet Based on Open Service

Improving performance of K-SVD based image denoising using curvelet transform

A well-balanced reaction-diffusion model for texture image denoising

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