Abstract
Since fourth order partial differential equations (PDEs) are able to avoid the blocky effects widely seen in images processed by second order PDEs, and nonlocal derivative (NLD) can effectively capture fundamental features such as edges, the improve PDE that is able to remove noise while preserving edge features will be built and its numerical scheme will be given. In the proposed model, the performances of removing noise and preserving edge are measured according to PSNR values and keeping edges index (KEI) respectively, and the experiments show that our method has a better performance than others.
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Zhou, C., Lui, S., Yan, T., Tao, W. (2014). Noise Removal Using Fourth Order PDEs Based on Nonlocal Derivative. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_73
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DOI: https://doi.org/10.1007/978-3-319-09333-8_73
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09332-1
Online ISBN: 978-3-319-09333-8
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