Research On Image Denoising Based On Nonsubsampled Contourlet Transform

Image inevitably affected by a variety of noise in the process of acquisition andtransmission, Noise leads to image deviates from the truth and seriously affect theimages of the understanding, analysis and the image subsequent processing(featureextraction and image segmentation). Therefore, there will be better protecting theimage quality and outstanding features of the image because image denoisingpretreatment before use the image. With the deepening of the study, the multiscalegeometric analysis is proposed and rapid development, has become a hot field, anda wide range of applications in the field of image denoising. This paper relatedresearch in image denoising applications based on the nonsubsampled contourlettransform. The main work of this paper includes:1. Describes the historical development process and image denoisingnonsubsampled contourlet transform principle and implementation process.2. A new image denoising algorithm based on classification standard andGeneralized Gaussian Distribution model in NonsubSampled ContourletTransform(NSCT) domain is proposed. Firstly, the noisy image is decomposed intoa set of multiscale and multidirectional frequency subbands by NSCT, according toclassification standard to each high frequency subband coefficients, then noncontained noisy NSCT coefficients are updated according to inverse nonsubsampledcontourlet transform is preformed to get denoised image. The simulationexperimental results show that the proposed method to better preserve the edge ofimage details, you can get a higher peak signal-to-noise ratio and visual effects.3. Multivariate the BKF model of image denoising method based on NSCTdomain is proposed. Many statistical methods used in the field of image denoising,but a lot of models only built in intrascale coefficient processing and ignoring thecorrelation interscale. This article fully considering correlation of internal andexternal scale under the multivariate the BKF model, the better image noiseremoval processing. The experimental results show that this method can be strongernoise suppression and preferably to maintain the original image of the structured.