Abstract
A novel rapid impulsive noise denoising method for range image is presented. In a 2D scan line acquired by a laser range finder, distribution features of impulsive noises (INs) are analyzed, and then a mathematical representation of the features is provided by defining a few new coefficients. Subsequently, a rule-based distinguishing criterion is formulated to detect two types of INs: dropouts and invaders. The traditional mean filter is improved by an automotive non-IN neighbor searching procedure. A compositive algorithm with a very low computational complexity has been implemented as an embedded module in our self-developed software with copyright. An experiment on real range image is performed, and the results indicate that the proposed method can detect all the impulsive noises accurately and denoise them with a significant efficiency. It is proven that the method is suitable for practical applications on industrial or other fields.
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Wang, J., Yao, Zq., Mei, L., Zhu, Yj., Yao, Y., Zhang, Y. (2013). Rapid Impulsive Noise Denoising in Range Images. In: Du, Z. (eds) Intelligence Computation and Evolutionary Computation. Advances in Intelligent Systems and Computing, vol 180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31656-2_40
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DOI: https://doi.org/10.1007/978-3-642-31656-2_40
Publisher Name: Springer, Berlin, Heidelberg
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