Convolutional Neural Network for Colorization of Black and White Photos
DOI:
https://doi.org/10.30812/matrik.v22i2.2652Keywords:
Convolutional Neural Network, Colorization, Deep Learning, Photos ColorizationAbstract
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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Detection,†Progress in Artificial Intelligence, vol. 9, no. 2, pp. 85–112, 2020.
[4] B. K. Triwijoyo, A. Adil, and A. Anggrawan, “Convolutional Neural Network With Batch Normalization for Classification of
Emotional Expressions Based on Facial Images,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer,
vol. 21, no. 1, pp. 197–204, 2021.
[5] F. F. Maulana and N. Rochmawati, “Klasifikasi Citra Buah Menggunakan Convolutional Neural Network,†Journal of Informatics
and Computer Science, vol. 01, pp. 104–108, 2019.
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A Survey,†Mechanical Systems and Signal Processing, vol. 151, p. 107398, 2021.
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Neural Network Applied to Fruit Image Processing,†Applied Sciences (Switzerland), vol. 10, no. 10, 2020.
[9] N. M. Trieu and N. T. Thinh, “Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional
Neural Network,†Applied Sciences (Switzerland), vol. 11, no. 22, 2021.
[10] M. Tripathi, “Analysis of Convolutional Neural Network Based Image Classification Techniques,†Journal of Innovative Image
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.
[12] A. Y. Muniar, P. Pasnur, and K. R. Lestari, “Penerapan Algoritma K-Nearest Neighbor pada Pengklasifikasian Dokumen Berita
Online,†Inspiration: Jurnal Teknologi Informasi dan Komunikasi, vol. 10, no. 2, p. 137, 2020.
[13] B. W. Kurniadi, H. Prasetyo, G. L. Ahmad, B. Aditya Wibisono, and D. Sandya Prasvita, “Analisis Perbandingan Algoritma
SVM dan CNN untuk Klasifikasi Buah,†Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA) Jakarta-
Indonesia, no. September, pp. 1–11, 2021.
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Neural Network,†vol. 22, no. 1, pp. 241–252, 2022.
[17] H. Yu, L. T. Yang, Q. Zhang, D. Armstrong, and M. J. Deen, “Convolutional Neural Networks for Medical Image Analysis:
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.
[19] P. Musa, E. P. Wibowo, S. B. Musa, and I. Baihaqi, “Pelican Crossing System for Control a Green Man Light with Predicted
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.
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Singapore, 2019, vol. 553.
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Trends in Information Technology and Engineering, ic-ETITE 2020, pp. 1–5, 2020.
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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.
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vol. 9, pp. 113 326–113 346, 2021.
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Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 9884–9893, 2021.
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2020.
[29] Y. Xiao, P. Zhou, Y. Zheng, and C. S. Leung, “Interactive Deep Colorization Using Simultaneous Global and Local Inputs,â€
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,â€
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,â€
Polibits, vol. 62, no. May, pp. 13–19, 2020.
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Published
2023-03-30
Issue
Section
Articles
How to Cite
[1]
S. U. Masruroh, A. Fiade, M. I. Tanggok, R. A. Putri, and L. A. Pratiwi, “Convolutional Neural Network for Colorization of Black and White Photos”, MATRIK, vol. 22, no. 2, pp. 393–404, Mar. 2023, doi: 10.30812/matrik.v22i2.2652.
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