Convolutional Neural Network for Colorization of Black and White Photos
Keywords:
Convolutional Neural Network, Colorization, Deep Learning, Photos Colorization
Abstract
People today are very fond of capturing moments by taking pictures. Various photo functions are used to document all forms of information that you want to store. In photos with digital images that have black and white, the information obtained is less than optimal, so an image processing process is needed to get color photos. Based on this, the author wants to change photos from black and white to color photos. The method used in this research is Convolutional Neural Network (CNN). This study uses Atlas 200 DK hardware and Ascend 310 processor. The data used in this study are 32 black and white photos in .jpg format as training data and perform 6 experimental scenarios with different numbers of black and white photos in each experiment. The total black and white photos used to experiment were 81 photos. The results obtained are models that successfully perform processing in the form of color photos with the appropriate color results in predicting the possible color of the object in each pixel in the photo. Based on this research, the trend of artificial intelligence can be implemented in changing the color of photos according to color predictions.
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Processing, vol. 3, no. 2, pp. 100–117, 2021.
[11] A. R. Lubis, M. Lubis, and Al-Khowarizmi, “Optimization of Distance Formula in K-Nearest Neighbor Method,” Bulletin of
Electrical Engineering and Informatics, vol. 9, no. 1, pp. 326–338, 2020.
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Neural Network,” vol. 22, no. 1, pp. 241–252, 2022.
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State-Of-The-Art, Comparisons, Improvement and Perspectives,” Neurocomputing, vol. 444, pp. 92–110, 2021.
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vol. 8, pp. 125 731–125 744, 2020.
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Age,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 2, pp. 293–306, 2022.
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Series, vol. 1528, no. 1, 2020.
[21] R.-s. Images, L. Hu, M. Qin, F. Zhang, Z. Du, and R. Liu, “RSCNN : A CNN-Based Method to Enhance Low-Light,” Remote
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Trends in Information Technology and Engineering, ic-ETITE 2020, pp. 1–5, 2020.
[24] D. R. Sarvamangala and R. V. Kulkarni, “Convolutional Neural Networks in Medical Image Understanding: A Survey,” Evolutionary
Intelligence, vol. 15, no. 1, 2022.
[25] S. B. Jadhav, V. R. Udupi, and S. B. Patil, “Identification of Plant Diseases Using Convolutional Neural Networks,” International
Journal of Information Technology (Singapore), vol. 13, no. 6, pp. 2461–2470, 2021.
[26] I. Zeger, S. Grgic, J. Vukovic, and G. Sisul, “Grayscale Image Colorization Methods: Overview and Evaluation,” IEEE Access,
vol. 9, pp. 113 326–113 346, 2021.
[27] L. Zhang, C. Li, E. Simo-Serra, Y. Ji, T. T.Wong, and C. Liu, “User-Guided Line Art Flat Filling with Split Filling Mechanism,”
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 9884–9893, 2021.
[28] S. Anwar, M. Tahir, C. Li, A. Mian, F. S. Khan, and A. W. Muzaffar, “Image Colorization: A Survey and Dataset,” pp. 1–20,
2020.
[29] Y. Xiao, P. Zhou, Y. Zheng, and C. S. Leung, “Interactive Deep Colorization Using Simultaneous Global and Local Inputs,”
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, pp. 1887–
1891, 2019.
[30] H. Kim, H. Y. Jhoo, E. Park, and S. Yoo, “Tag2pix: Line Art Colorization Using Text Tag With SECAT and Changing Loss,”
Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-Octob, pp. 9055–9064, 2019.
[31] S. Rajpurohit, S. Patil, N. Choudhary, S. Gavasane, and P. Kosamkar, “Identification of Acute Lymphoblastic Leukemia in
Microscopic Blood Image Using Image Processing and Machine Learning Algorithms,” 2018 International Conference on
Advances in Computing, Communications and Informatics, ICACCI 2018, no. Cll, pp. 2359–2363, 2018.
[32] B. K. Hatuwal, A. Shakya, and B. Joshi, “Plant Leaf Disease Recognition Using Random Forest, KNN, SVM and CNN,”
Polibits, vol. 62, no. May, pp. 13–19, 2020.
Published
2023-03-30
How to Cite
Masruroh, S., Fiade, A., Tanggok, M., Putri, R., & Pratiwi, L. (2023). Convolutional Neural Network for Colorization of Black and White Photos. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(2), 393-404. https://doi.org/https://doi.org/10.30812/matrik.v22i2.2652
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