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Implementation of image colorization with convolutional neural network

Author

Listed:
  • Chetna Dabas

    (Jaypee Institute of Iinformation Technology)

  • Shikhar Jain

    (Jaypee Institute of Iinformation Technology)

  • Ashish Bansal

    (Jaypee Institute of Iinformation Technology)

  • Vaibhav Sharma

    (Jaypee Institute of Iinformation Technology)

Abstract

Huge amount of work is getting done on Image colorization worldwide. This research paper proposes a model for image colorization while making use of fully automatic Convolutional Neural Network. Image colorization processes a daunting task, and this research paper proposes a relevant model for the prediction of A and B models for LAB color space and it makes a direct use the lightness channel. In this work, a pre-trained VGG-16 network was used for semantically interpreting the concepts associated with images and coloring the images. In the proposed work, the convolutional layer has been fused with the max pooling layer (higher one) of the VGG network. Architecture of the proposed model has been presented. The experimentation has been carried out with varying objective functions. LaMem experimental dataset has been used in this work in order to validate the proposed model. The proposed model is evaluated and results are visualized by histograms for true and predicted images for RGB values. Further, the proposed model has been compared with the existing models and performs better in terms of execution times (in s) for different image sizes and the results are tabulated.

Suggested Citation

  • Chetna Dabas & Shikhar Jain & Ashish Bansal & Vaibhav Sharma, 2020. "Implementation of image colorization with convolutional neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 625-634, June.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:3:d:10.1007_s13198-020-00960-5
    DOI: 10.1007/s13198-020-00960-5
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    References listed on IDEAS

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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
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