Dense disparity estimation based on the bi-dimensional empirical mode decomposition and Riesz transformation

Feifei Gu; Hong Zhao; Zhan Song; Juan Zhao

doi:10.1117/12.2315662

24 May 2018 Dense disparity estimation based on the bi-dimensional empirical mode decomposition and Riesz transformation

Feifei Gu, Hong Zhao, Zhan Song, Juan Zhao

Author Affiliations +

Proceedings Volume 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V; 1067927 (2018) https://doi.org/10.1117/12.2315662
Event: SPIE Photonics Europe, 2018, Strasbourg, France

Abstract

This paper proposed to apply the Bi-dimensional Empirical Mode Decomposition (BEMD) to the dense disparity estimation problem. The BEMD is a fully data-driven method and does not need predetermined filter and wavelet functions. It is locally adaptive and has obvious advantages in analyzing non-linear and non-stationary signals. Firstly we decompose the original stereo images by 2D-sifting process of the BEMD respectively. Through this procedure, a serial of Intrinsic Mode Functions (IMFs) and a residue are achieved. The residue denotes the DC component of the signal. Secondly, subtract the residue from original image. The resulting two dimensional signals can be thought of being free of disturbing frequencies, such as noise and illumination components. Subsequently, to obtain robust local structure information of the images, the plural Riesz transformation is utilized to achieve corresponding 2D analytic signals of the images. Thirdly, extract local phase information of the analytic signals. The similarity of local phase of stereo images, instead of local intensity information, are taken as the basis of calculating matching cost, which could reveal local structure with more robustness. At last, dense disparity map is estimated based on the proposed method. The winnertakes-all (WTA) strategy is applied to compute disparity of each pixel separately. Comparative experiment is conducted to compare the performance of the method with intensity-based methods. Rather good results have been achieved.

Citation Download Citation

Feifei Gu, Hong Zhao, Zhan Song, and Juan Zhao "Dense disparity estimation based on the bi-dimensional empirical mode decomposition and Riesz transformation", Proc. SPIE 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V, 1067927 (24 May 2018); https://doi.org/10.1117/12.2315662

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KEYWORDS

Image processing

Signal processing

Signal analyzers

Wavelets

Filtering (signal processing)

Fourier transforms

Image filtering

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