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《Journal of South China University of Technology(Natural Science Edition)》 2014-01
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Denoising of Coal Combustion Flame Images Based on HMT Model in Dual-Tree Complex Wavelet Domain

Wu Yi-quan;Song Yu;College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics;State Key Laboratory of Coal Combustion,Huazhong University of Science and Technology;  
In order to effectively eliminate the noises existing in boiler coal combustion flame images that are unfavorable for the subsequent image feature extraction and temperature reconstruction,an image denoising method based on the HMT( Hidden Markov Tree) model in dual-tree complex wavelet domain is proposed. In this method, first,a dual-tree complex wavelet transform is performed for noisy flame image. Next,the real part and the imaginary part of the dual-tree complex wavelet coefficients are respectively modeled according to the HMT model. Then, the model parameters are estimated by using the expectation maximization algorithm,and the noiseless dual-tree complex wavelet coefficients are estimated according to the Bayes minimum mean square error( MMSE) criterion. Finally,an inverse dual-tree complex wavelet transform is conducted to obtain denoised flame images. Experimental results show that the proposed method is superior to the wavelet VisuShrink threshold method and the method based on HMT model in wavelet or Contourlet domain because it helps to reduce the noise more effectively and achieve higher peak signal-to-noise ratio.
【Fund】: 国家自然科学基金资助项目(60872065);; 华中科技大学煤燃烧国家重点实验室开放基金资助项目(FSKLCC1001);; 江苏省高校优势学科建设工程资助项目
【CateGory Index】: TP391.41
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