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Volume 36 Issue 2
Jan.  2012
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Infrared image denoising based on total variation theory

  • Corresponding author: QIAN Jian-sheng, qianjsh@cumt.edu.cn
  • Received Date: 2011-08-08
    Accepted Date: 2011-09-08
  • In order to remove the noise of infrared images,an image denoising method based on total variation was proposed,which inherits the advantage of edge preserving of the total variation(TV)model.A novel diffusion function was derived from the theory of smooth diffusion.Simultaneously,an edge detector operator was introduced to improve the related parameters of regularization term and fidelity term,which makes the denoised image avoid the staircase effect to a great extent.Finally,the implement of the algorithm was derived and the experimental results demonstrated the superior performance of the modified denoising algorithm.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Infrared image denoising based on total variation theory

    Corresponding author: QIAN Jian-sheng, qianjsh@cumt.edu.cn
  • 1. School of Computer Science and Technology, China University of Mining & Technology, Xuzhou 221008, China;
  • 2. State Development & Investment Corp., Huainan 232001, China;
  • 3. Shandong Longji Machinery Co., Ltd, Longkou 265700, China;
  • 4. Advanced Analysis & Computation Center, China University of Mining & Technology, Xuzhou 221008,China;
  • 5. School of information and Electrical Engineering China University of Mining & Technology, Xuzhou 221008, China

Abstract: In order to remove the noise of infrared images,an image denoising method based on total variation was proposed,which inherits the advantage of edge preserving of the total variation(TV)model.A novel diffusion function was derived from the theory of smooth diffusion.Simultaneously,an edge detector operator was introduced to improve the related parameters of regularization term and fidelity term,which makes the denoised image avoid the staircase effect to a great extent.Finally,the implement of the algorithm was derived and the experimental results demonstrated the superior performance of the modified denoising algorithm.

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