Parameter optimization for image denoising based on block matching and 3D collaborative filtering

Pedada R., Kuğu E., Li J., Yue Z., Shen Y.

Medical Imaging 2009 - Image Processing, Lake Buena Vista, FL, United States Of America, 8 - 10 February 2009, vol.7259 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 7259
  • Doi Number: 10.1117/12.812202
  • City: Lake Buena Vista, FL
  • Country: United States Of America
  • Keywords: Block matching, Collaborative filtering, Cost functions, Multiobjective optimization, Pareto front
  • TED University Affiliated: No


Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) is utilized to simultaneously optimize the cost functions by modifying parameters associated with the denoising methods. The effectiveness of the algorithm is demonstrated by applying the proposed optimization procedure to enhance the image denoising results using block matching and 3D collaborative filtering. Experimental results show that the proposed optimization algorithm can significantly improve the performance of image denoising methods in terms of noise removal and structure information preservation. © 2009 Copyright SPIE - The International Society for Optical Engineering.