Dissertation > Excellent graduate degree dissertation topics show

Multiplicative Noise Removal Based on Split-Bregman Algorithm

Author: WangCuiPing
Tutor: PanZhenKuan
School: Qingdao University
Course: Applied Computer Technology
Keywords: image denoising multiplicative noise Split-Bregman algorithm color image
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 91
Quote: 0
Read: Download Dissertation

Abstract


Noise removal is one of the fundamental tasks of image restoration which can get clear images from the corrupted ones by different noises. The researches on variational models of additive noise removal are successful, but the investigation of multiplicative noise removal is late. The variational models for multiplicative noise reduction are deeply discussed in this paper, and some solutions to existing problems are put forward. And several aspects are introduced as follows:Firstly, Split-Bregmen algorithm transforms original variational models into solutions of simple Poisson equations and generalized soft thresholding formulas by introducing auxiliary variables. In this way, the calculation speed of variational models is improved. Moreover, Bregman iteration speeds up the convergence rate of energy functional and enhances restoration quality. Secondly, a general variational model for different cases of multiplicative noise removal is proposed, which includes a data term and a regularization term. The data term can be derived from Gauss, Rayleigh, Gamma, Poisson distribution of noises, the regularization term can be TV (Total Variation). PM (Perona and Malik) and Charbonnier regulerizers. The model is tested through numerical experiments on different combinations of data terms and regularization terms. Thirdly, TV model of grayscale images is extended to color images based on analysis of principles that color image noise reduction should follow. A general model for multiplicative noise removal of color images and related Split-Bregman algorithm is designed. Some experiments on color images corrupted by additive and multiplicative noise are implemented to verify the effectiveness of Split-Bregman methods.

Related Dissertations

  1. Research on Methods of Medical Ultrasound Image Denoising,TP391.41
  2. Research on Contourlet Transform and Its Application on Image Processing,TP391.41
  3. Color Image Compression Based on Quaternion Neural Network,TP391.41
  4. Research and Realization of Image Denoising Based on Wavelet Transform,TP391.41
  5. Research on Simulations and Modified Algorithms of Median Filter,TP391.41
  6. Studies of Technologies and Methods on Image Mosaic,TP391.41
  7. Research on Adaptive Median Filtering Algorithm Applied in Image Processing,TP391.41
  8. Based on the difference between the pixel grayscale image denoising wavelet edge detection and combining adjacent to its,TP391.41
  9. Variational model for image restoration Split-Bregman algorithm,TP391.41
  10. Variational multiphase image segmentation model and Split Bregman iterative algorithm,TP391.41
  11. The surface geometry noise removal of non- local variational model,TP391.41
  12. Wavelets-based Signal Sparse Representation and Its Application in Image Denoising,TP391.41
  13. Study on Image Enhancement Method Based on Partial Differential Equation,TP391.41
  14. A Study on Image Denoising Based on Biorthogonal Wavelet Transform,TP391.41
  15. Research on Parallel Method for Image Denoising Via Sparse Representations,TP391.41
  16. Model based on visual characteristics and image enhancement algorithms and performance analysis,TP391.41
  17. PDE-based image denoising and enhancement of,TP391.41
  18. Image Denoising and Enhancement based on Curvelet Transform,TP391.41
  19. Study of Numerically Stable Estimation for Singular Systems with Multiplicative Noise,TN911.7
  20. Study on Robust State Estimation Algorithm for Systems with Multiplicative Noise,TP13
  21. Research on Technology of Image Segmentation Based on Level Set Method,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