Abstract
A large range of methods covering various fields of mathematics are available for denoising an image. The initial denoising models are derived from energy minimization using nonlinear partial differential equations (PDEs). The filtering models based on smoothing operators have also been used for denoising. Among them the recently developed nonlocal means method proposed by Buades, Coll and Morel in 2005 is quite successful. Though the method is very accurate, it is very slow and hence quite impractical. In 2008, Gilboa and Osher extended some known PDE and variational techniques in image processing to the nonlocal framework and proposed the nonlocal total variation method for Gaussian noise. We used this idea to develop a nonlocal model for speckle noise. Here we have extended the speckle model introduced by Krissian et al. in 2005 to the nonlocal framework. The Split Bregman scheme is used solve this new model.
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Misra, A.B., Lim, H. (2013). Nonlocal Total Variation Based Speckle Denoising Model. In: S, M., Kumar, S. (eds) Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012). Lecture Notes in Electrical Engineering, vol 221. Springer, India. https://doi.org/10.1007/978-81-322-0997-3_46
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DOI: https://doi.org/10.1007/978-81-322-0997-3_46
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