Video denoising based on adaptive shrinkage in surfacelet transform domain

  • Authors:

  • Xiaolin Tian;Jie Li;Licheng Jiao

  • Affiliations:

  • Key Laboratory of Intelligent Perception and, Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and, Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and, Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China

  • Venue:
  • IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
  • Year:
  • 2011

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Abstract

A video denoising algorithm based on adaptively shrinking surfacelet transform (ST) coefficient is proposed. Firstly, the probability distribution feature of noise in ST domain is researched through Monte Carlo method, based on which the threshold of each ST coefficient and the mask classification can be obtained. The threshold is applied to construct energy ratio of each ST coefficient, which is equivalent to likelihood ratio. The mask classification is used to construct prior ratio of each ST coefficient. Based on the construction of three-dimensional and directional neighborhood, the ideal neighborhood shape is selected, so that optimal prior ratio is obtained. The energy ratio and prior ratio are combined and applied into shrinkage estimator, which can be used to shrink the ST coefficients. Finally, the denoised video is achieved by inverse surfacelet transform using shrunk ST coefficients. Experimental results show a superior visual and quantitative performance of the proposed method for various levels of noise and motion.