ICIP 2006, Atlanta, GA

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Session:Denoising - I
Time:Tuesday, October 10, 09:40 - 12:20
Presentation: Poster
Authors: Jin Wang; Xuzhou Normal University 
 Yanwen Guo; Nanjing University 
 Yiting Ying; Zhejiang University 
 Yanli Liu; Zhejiang University 
 Qunsheng Peng; Zhejiang University 
Abstract: For the non-local denoising approach presented by Buades et al., remarkable denoising results are obtained at high expense of computational cost. In this paper, a new algorithm that reduces the computational cost for calculating the similarity of neighborhood windows is proposed. We first introduce an approximate measure about the similarity of neighborhood windows, then we use an efficient Summed Square Image (SSI) scheme and Fast Fourier Transform (FFT) to accelerate the calculation of this measure. Incorporated with SSI, we also propose an efficient noise deviation estimation method for noisy images. Our algorithm is about fifty times faster than the original non-local algorithm both theoretically and experimentally, yet produces comparable results in terms of mean-squared error (MSE) and perceptual image quality.