Dissertation > Excellent graduate degree dissertation topics show

Research on Image Denoising Methods Based on Contourlet Transform

Author: HeYaLi
Tutor: YangHuiXian
School: Xiangtan University
Course: Physical Electronics
Keywords: Image denoising Multiscale geometric analysis Contourlet transform Adaptive threshold Morphological filter
CLC: TP391.41
Type: Master's thesis
Year: 2013
Downloads: 64
Quote: 0
Read: Download Dissertation

Abstract


The digital images are gradually becoming a major source of human accessing toinformation. And the process of image acquisition and transmission is ofteninfluenced by external factors, so that the image is polluted by noise. In order toeffectively extract the information from noisy image, it needs the help of imageprocessing technology to reduce noise. This paper has introduced image denoisingalgorithm based on wavelet transform. On this basis, the research of image denoisingalgorithm in Contourlet transform framework has studied as follows:(1) The significance of research background and the subject selection of digitalimage denoising are elaborated, and the development of image denoising fromwavelet transform to multiscale geometric analysis method is introduced. Thetheoretical foundation and structure focused on contourlet transform is discussed,which provides theoretical guidance for further research of image denoising algorithmunder the framework of the contourlet transform.(2) Two key factors of classic threshold denoising algorithm based on wavelettransform are introduced, which are selection of the threshold and threshold function.And the advantages and disadvantages of the different threshold and thresholdfunction are analyzed respectively. In addition, an overview of the basic operations ofthe morphology and the selection rules of structuring element is provided, whichdemonstrates the affect of image denoising in morphological filtering.(3) The frame of Contourlet transform has a very good directional property, but inthe classic threshold denoising algorithm, the threshold processing of all the highfrequency coefficients uses the same of threshold and neglects the correlation ofcontourlet coefficients. Therefore, concerning the defects of multi-scale threshold ondirectional information using Contourlet transform, a new adaptive thresholddenoising algorithm was proposed, which was based on average energy ofneighboring window. According to the distribution of the coefficients energy, theContourlet coefficients were divided up into three areas. The noise could be decreasedobviously by adjusting the threshold of these areas with different variables.(4) While adding the energy of coefficients in neighborhood to thresholdevaluation, although the threshold has considered with neighborhood information ofcontourlet coefficients, the handling capacity of denoising algorithm for high-noise image is still limited. Through analysis and comparison, it proves that usingmorphological filter is an effective method for image denoising. Firstly, the wholealgorithm is parallel consists of two channels. Secondly, the details of the structureelements are added to the structuring elements of morphology. Finally, preprocessedimages with Contourlet transform threshold denoising has fused to the final denoisingimage.

Related Dissertations

  1. Research on Spread Spectrum Code Acquisition of Double Threshold Based on Sliding Correlator,TN914.42
  2. Research on Methods of Medical Ultrasound Image Denoising,TP391.41
  3. Research on the Watermark Method Based on HVME and Contourlet,TP309.7
  4. Research on Tracking and Registration Algorithm in Augmented Reality,TP391.9
  5. Based on Contourlet Transform Digital Watermarking Method,TP309.7
  6. Research on Blind Watermarking Technique of Digital Images Based on Contourlet Transform,TP309.7
  7. The Research of Medical Ultrasound Image Denosing Algorithms Based on Multiscale Geometric Analysis,TP391.41
  8. Research on Contourlet Transform and Its Application on Image Processing,TP391.41
  9. Study on Fabric Defect Detection and Sutomati Grad-ing System,TP391.41
  10. Multi-source image fusion technology research,TP391.41
  11. Research and Realization of Image Denoising Based on Wavelet Transform,TP391.41
  12. Research on Simulations and Modified Algorithms of Median Filter,TP391.41
  13. Research on Adaptive Median Filtering Algorithm Applied in Image Processing,TP391.41
  14. Based on the difference between the pixel grayscale image denoising wavelet edge detection and combining adjacent to its,TP391.41
  15. Multiplicative Noise Removal Based on Split-Bregman Algorithm,TP391.41
  16. Wavelets-based Signal Sparse Representation and Its Application in Image Denoising,TP391.41
  17. A Study on Image Denoising Based on Biorthogonal Wavelet Transform,TP391.41
  18. Research and Application of Illuminant Invariant Features in Image Retrieval and Recognition,TP391.41
  19. Tetrolet sparse regularization learning with the sample image super-resolution algorithm,TP391.41
  20. A Research of Image Denoising and Restoration Based on Total Variation Method,TP391.41
  21. Research on Image Noise Inhibition Based on Fractional Order Lower Statistics,TP391.41

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile