Abstract
This paper presents both standard and adaptive versions of regularized surface smoothing algorithms for 3D image enhancement. We incorporated both area decreasing flow and the median constraint as multiple regularization functionals. The corresponding regularization parameters adaptively changes according to the local curvature value. The combination of area decreasing flow and the median constraint can efficiently remove various types of noise, such as Gaussian, impulsive, or mixed types. The adaptive version of the proposed regularized smoothing algorithm changes regularization parameters based on local curvature for preserving local edges and creases that reflects important surface information in 3D data. In addition to the theoretical expansion, experimental results show that the proposed algorithms can significantly enhance 3D data acquired by both laser range sensors and disparity maps from stereo images.
This work was supported in part by Korean Ministry of Science and Technology under the National Research Lab. Project, in part by Korean Ministry of Education under Brain Korea 21 Project, and in part by grant No.R08-2004-000-10626-0 from the Basic Research Program of the Korea Science & Engineering Foundation.
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Ki, H., Shin, J., Jung, J., Lee, S., Paik, J. (2004). Surface Smoothing for Enhancement of 3D Data Using Curvature-Based Adaptive Regularization. In: Klette, R., Žunić, J. (eds) Combinatorial Image Analysis. IWCIA 2004. Lecture Notes in Computer Science, vol 3322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30503-3_35
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DOI: https://doi.org/10.1007/978-3-540-30503-3_35
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