Image Demosaicking via Chrominance Images with Parallel Convolutional Neural Networks

Takuro Yamaguchi, Masaaki Ikehara

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Many conventional demosaicking methods are based on hand-crafted filters. However, the filters yield false colors in salient regions like edges and textures. For acquisition of high quality images, we focus on neural networks. Neural networks lead to high accuracy in many fields. However, there are few methods in demosaicking field. For adaptation to demosaicking, we consider not only network's architecture but also the input. In this research, we utilize a Bayer image as input of our networks. However, different filter is needed in estimation at different color pixels, for example, missing red value at green pixel and that at blue pixel. Therefore, we prepare four networks with downsampling operators classified by color patterns in Bayer images. This downsampling operator not only identifies the color pattern but also reduces the calculation cost in each network due to reduction of the size of feature maps. Besides, preparation of multi-networks instead of a deep single-network is suitable for today's parallel computing. Moreover, we utilize not missing color images but chrominance images as output. Compared to results with missing color images as output, the results with chrominance images obtains higher accuracy. Experimental results show our CNN-based approach produces high quality restored images.

    Original languageEnglish
    Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1702-1706
    Number of pages5
    ISBN (Electronic)9781479981311
    DOIs
    Publication statusPublished - 2019 May 1
    Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
    Duration: 2019 May 122019 May 17

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    Volume2019-May
    ISSN (Print)1520-6149

    Conference

    Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
    CountryUnited Kingdom
    CityBrighton
    Period19/5/1219/5/17

    Keywords

    • Convolutional Neural Network
    • Demosaicking
    • Multi-network
    • Parallel Computing

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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  • Cite this

    Yamaguchi, T., & Ikehara, M. (2019). Image Demosaicking via Chrominance Images with Parallel Convolutional Neural Networks. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 1702-1706). [8682874] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8682874