Fast 2-D Bilateral Filtering Method without Kernel-size Dependency

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  • Igarashi Masaki
    Graduate School of Information Science and Technology, Hokkaido University
  • Ikebe Masayuki
    Graduate School of Information Science and Technology, Hokkaido University
  • Shimoyama Sousuke
    Graduate School of Information Science and Technology, Hokkaido University
  • Yamano Kenta
    Graduate School of Information Science and Technology, Hokkaido University
  • Motohisa Junichi
    Graduate School of Information Science and Technology, Hokkaido University

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  • イメージセンシング技術とその応用　　フィルタカーネルサイズの依存性を持たない高速２‐Ｄバイラテラルフィルタ演算手法の提案
  • フィルタカーネルサイズの依存性を持たない高速2-Dバイラテラルフィルタ演算手法の提案
  • フィルタカーネルサイズ ノ イソンセイ オ モタナイ コウソク 2 Dバイラテラルフィルタ エンザン シュホウ ノ テイアン

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Abstract

We propose a fast 2-D bilateral filtering method without kernel-size dependency. Focusing on weighted local histograms and the central limit theorem combined with applying line buffers of column histograms, enables us to reduce the number of necessary memory accesses and calculations. Numerical experiments demonstrated a reduction in both calculations and kernel-size independency. We used a dual core 2-GHz CPU with our method and were able to achieve one million pixels per 0.5 sec operation without the need for downsampling, Single Instruction/Multiple Data (SIMD) or multi-thread operation.

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