Hardware-efficient color correlation–adaptive demosaicing with multifiltering

Seung Hyun Lee; Dong Yoon Choi; Byung Cheol Song

doi:10.1117/1.JEI.28.1.013018

31 January 2019 Hardware-efficient color correlation–adaptive demosaicing with multifiltering

Seung Hyun Lee, Dong Yoon Choi, Byung Cheol Song

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 1, 013018 (January 2019). https://doi.org/10.1117/1.JEI.28.1.013018

Abstract

Color demosaicing is a key image processing step aiming to reconstruct the missing pixels from a recorded raw image that has a color filter array (CFA) pattern. The color correlation–based guided filters, such as minimized-Laplacian residual interpolation (MLRI), are known as the state-of-the-art demosaicing techniques. However, in the conventional guided filter-based techniques, the artifacts are generated in areas with low color correlation. Furthermore, a large number of line memories are required in the hardware implementation of a conventional guided filter-based technique because of the large effective field size. To overcome these two problems, we propose a color correlation–adaptive demosaicing algorithm that selectively applies a specific intracolor demosaicing to regions with low color correlation. We also propose an algorithm structure that reduces the effective field size in the vertical direction to reduce the number of line memories, while maintaining the image quality performance during the hardware implementation. The experimental results show that the proposed scheme can reduce the line memory to one-third while showing marginal performance degradation compared to the state-of-the-art MLRI weighted framework in terms of the color peak signal-to-noise ratio for the IMAX datasets.

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Seung Hyun Lee, Dong Yoon Choi, and Byung Cheol Song "Hardware-efficient color correlation–adaptive demosaicing with multifiltering," Journal of Electronic Imaging 28(1), 013018 (31 January 2019). https://doi.org/10.1117/1.JEI.28.1.013018

Received: 25 June 2018; Accepted: 3 January 2019; Published: 31 January 2019

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