Polynomial fitting for period prediction in sliding-DCT-based filtering

Kazuya Ishikawa; Yuto Sumiya; Norishige Fukushima

doi:10.1117/12.2626084

30 April 2022 Polynomial fitting for period prediction in sliding-DCT-based filtering

Kazuya Ishikawa, Yuto Sumiya, Norishige Fukushima

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121770S (2022) https://doi.org/10.1117/12.2626084
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Gaussian filtering (GF) is a fundamental smoothing filter that determines the weights in the kernel according to the Gaussian distribution. GF is an essential tool in image processing and is used in various applications. Therefore, accelerating GF is essential in various situations. The sliding DCT-based GF is one of the fastest methods for approximating GF. The Gaussian kernel is decomposed into multiple cosine kernels using the DCT transform and is approximated by the limited number of kernels. When calculating the period of the DCT for fitting the best length, a linear search method is used; however, the brute-force search has a significant impact on the filtering processing time. In this paper, we accelerate the period estimation by polynomial fitting. Experimental results show that the proposed method has almost the same accuracy as the brute-force approach.

Citation Download Citation

Kazuya Ishikawa, Yuto Sumiya, and Norishige Fukushima "Polynomial fitting for period prediction in sliding-DCT-based filtering", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121770S (30 April 2022); https://doi.org/10.1117/12.2626084

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KEYWORDS

Gaussian filters

Optical filters

Image filtering

Image processing

Finite impulse response filters

Convolution

Matrices

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