Enhancing the Quality of Cellular Camera Video With Convolutional Neural Network

Main Article Content

Hotman Parsaoran Tampubolon Watas Sinurat Steven Eduard Gulo Befi Juniman Gulo Amir Mahmud Husein
Corresponding Author:
Hotman Parsaoran Tampubolon | hotmant97@gmail.com

Copyright (C):
Hotman Parsaoran Tampubolon, Watas Sinurat, Steven Eduard Gulo, Befi Juniman Gulo, Amir Mahmud Husein


Abstrak— At present technological developments, especially in the field of computer vision, are showing significant performance such as the application of convolutional neural networks that have a very high degree of accuracy, for example improving video quality which recently has image restoration such as super resolution (VSR) thanks to deep learning with the aim of helping produce better visual videos. The use of video cameras for mobile devices is now increasingly highly developed. Nowadays mobile devices are experiencing a rapid increase in quality especially in cameras. However, physical limitations such as the small sensor size, compact lens and the lack of supporting hardware can prevent cellular devices from achieving good video camera quality results. For that many method approaches are applied, one of which is the CNN (Convolutional Neural Network) method. This method can improve the image of video recordings that have poor quality.

Keywords—Convolutional neural network, computer vision, Improved video quality ;

Keyword: Convolutional neural network, computer vision, Improved video quality ;


Download data is not yet available.

Article Details

How to Cite
TAMPUBOLON, Hotman Parsaoran et al. Enhancing the Quality of Cellular Camera Video With Convolutional Neural Network. SinkrOn, [S.l.], v. 4, n. 1, p. 202-206, oct. 2019. ISSN 2541-2019. Available at: <https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10239>. Date accessed: 16 july 2020. doi: https://doi.org/10.33395/sinkron.v4i1.10239.
* Abstract viewed = 46 times PDF downloaded = 53 times *


Ahn, N., Kang, B., & Sohn, K.-A. (2019). Efficient Deep Neural Network for Photo-realistic Image Super-Resolution.

Caballero, J., Ledig, C., Aitken, A., Acosta, A., Totz, J., Wang, Z., & Shi, W. (2017). Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation. CPVR (pp. 4778-4787). CVPR.

Harahap, M., Husein, A. M., & Dharma, A. (2017). IDENTIFIKASI TANDA TANGAN DENGAN KOHONEN SOM BERBASIS PRINCIPAL COMPONENT ANALYSIS. Semnastikom (pp. 333-337). Jayapura: Aptikom.

Haris, M., Shakhnarovich, G., & Ukita, N. (2019). Recurrent Back-Projection Network for Video Super-Resolution. CPVR (pp. 3897-3906). CPVR.

Husein, A. M., & Harahap, M. (2017). Penerapan Metode Distance Transform Pada Kernel Discriminant Analysis Untuk Pengenalan Pola Tulisan Tangan Angka Berbasis Principal Component Analysis. SinkrOn, 31-36.


Husein, A. M., Calvin, C. H., Raymond, L., & William, W. (2018). Motion detect application with frame difference method on a surveillance Camera. International Conference on Mechanical, Electronics, Computer, and Industrial Technology (2018) (pp. 1-10). Medan: Universitas Prima Indonesia.

Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., & Gool, L. V. (2018). WESPE: Weakly Supervised Photo Enhancer for Digital Cameras. CVPR, (pp. 804-813).

Jeong, J., Park, H., & kwak, N. (2017). Enhancement of SSD by concatenating feature maps for object detection. British Machine Vision Conference (BMVC) (pp. 76.1-76.12). London: BMVA Press.

Kim, S. Y., Lim, J., Na, T., & Kim, M. (2018). 3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks. ArXiv.

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (pp. 1-14). IEEE.

Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A. P., Bishop, R., . . . Wang, Z. (2016). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. ArXIV, (pp. 1-10).

Vorobjov, D., Zakharava, I., Bohush, R., & Ablameyko, S. (2018). An effective object detection algorithm for high resolution video by using convolutional neural network. 15th International Symposium on Neural Networks, ISNN 2018, (pp. 503-510). Minsk, Belarus.

Wijaya, B. A., Husein, A. M., & Harahap, M. K. (2017). Implementation Distance Transform Method in Kernel Discriminant Analysis for Face Recognition Using Kohonen SOM. International Journal of Engineering Research & Technology (IJERT), 28-31.

Zhang, S., Wen, L., Bian, X., Lei, Z., & Z, S. (2018). Single-Shot Refinement Neural Network for Object Detection. CVPR (pp. 4203-4212). Salt Lake: CVPR.

Most read articles by the same author(s)