Fast image blurring using Lookup Table for real time feature extraction

In this paper, a novel fast Gaussian blurring method that uses look-up table (LUT) is presented for creating a real time difference of Gaussian (DoG) pyramid. The LUT for fast and accurate Gaussian blur is obtained from the values calculated from one-dimensional(1D) Gaussian elements and pixel values of image. To evaluate the method, computation time and similarity with a conventional two-dimensional(2D) Gaussian blur are measured, and compared to other blurring methods. Based on the computational results, the proposed method shows good performance than other blurring methods such as box filter, Stack-blur, Romain Guy's method, and recursive Gaussian blur. The proposed method could be effectively applied to create the DoG pyramid for feature extraction.