Research And Application Of Digital Image De-noising Algorithm

Digital image noise mainly originates from the process of image acquisition and transmission, which also is one of the main factors influencing the quality of image information. Therefore image de-noising is a popular research subject in image processing area. This paper has researched some crucial techniques and algorisms deeply on this area. The main work and research results will be given as follows:Firstly this paper provides an overview of development and current situation on image de-noising, and proposes some latest research area, gives some common image statistical mathematical model and the standard of judgment of de-noising quality, then introduces two classical de-noising algorithms :media filter method and wiener filter method, analyses and discusses their de-noising principles and properties emphatically.Secondly this paper discusses two latest image analysis tools: wavelet transform and Multi-scale Geometry transform, discusses how to apply this two transform on image de-noising, analyses their spreading property of coefficients and threshold choosing principles; on this basic this paper proposes a hierarchical threshold de-noising method based on Contourlet transform, carries on simulation experiment to these algorithm and compares their de-noising results.Finally according to the drawbacks of Contourlet transform, this paper proposes an improved Contourlet transform: multi wavelet-nonsubsampled Contourlet transform, this algorithm uses multi wavelet for multi-scale decomposing and combines nonsubsampled filter banks for multi-direction decomposing, applies this transform on image de-noising and improves Bayes Shrink Adaptive Threshold method according to the relation between the decomposing sub bands. So this paper proposes a new algorithm which is called multi wavelet-nonsubsampled Contourlet transform de-noising algorithm. The experiment result shows this algorithm is superior to some traditional algorithm that can reserve more image detail information and get a better de-noising effect, which also has certain of application value.