Improvement Of Image Quality Using Convolutional Neural Networks Method
(1) Universitas Jenderal Soedirman
(2) Universitas Jenderal Soedirman
(3) Confusion Matrix in Machine Learning
Abstract
Abstract.
Purpose: This desire for high resolution stems from two main application areas, namely improving pictorial information for human interpretation and assisting automatic machine perception in representing images or videos. Image resolution describes the detail contained in an image, the higher the resolution, the more detail there is. The resolution of a digital image can be classified into various types, namely pixel resolution, spatial resolution, temporal resolution, and radiometric resolution. In this context, we are interested in spatial resolution.
Methods: Elements of a digital image consist of a collection of small images called pixels. Spatial resolution refers to the pixel density of an image and is measured in pixels per unit area. A quality digital image is determined by the size of the resolution it has. A low resolution or low-resolution is a drawback of a digital image because the information contained in the image means little compared to a high-resolution image.
Result: Therefore, in this study, a digital image processing program was created in the form of Image Super-Resolution with the Convolutional Neural Network method to utilize low-resolution images to produce high-resolution images. With a fairly short training process, namely 6050 datasets with 100 CNN epochs, the average PSNR image is 5% higher.
Novelty: Image quality can be improved by changing the parameters in the CNN method so that image quality can be improved.
Keywords
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P. Liu, Y. Hong, and Y. Liu, “Deep differential convolutional network for single image super-resolution,” IEEE Access, vol. 7, no. 1, pp. 37555–37564, 2019.
K. H. and X. T. C. Dong, C. C. Loy, “Image Super-Resolution Using Deep Convolutional Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 295–307, 2016.
S. Ayas and M. Ekinci, “Single image super resolution using dictionary learning and sparse coding with multi-scale and multi-directional Gabor feature representation,” Inf. Sci. (Ny)., vol. 512, pp. 1264–1278, 2020.
C. Feng, M. & Zhao, S. & Xing, “Image quality assessment using PSNR based on visual feature,” J. Nanjing Univ. Posts Telecommun. (Natural Sci., vol. 35, no. 4, pp. 33–38, 2015.
C. K. and Z. D. O. Keleş, M. A. Yιlmaz, A. M. Tekalp, “On the Computation of PSNR for a Set of Images or Video,” 2021.
R. Fan, S. Li, G. Lei, and G. Yue, “Shallow and deep convolutional networks for image super-resolution,” Proc. - Int. Conf. Image Process. ICIP, vol. 2017-September, pp. 1847–1851, 2018.
X. Ji, Y. Lu, and L. Guo, “Image super-resolution with deep convolutional neural network,” Proc. - 2016 IEEE 1st Int. Conf. Data Sci. Cyberspace, DSC 2016, pp. 626–630, 2017.
X. Shang et al., “A New Combined PSNR For Objective Video Quality Assessment School of Communication and Information Engineering , Shanghai University , China School of Engineering Science , Simon Fraser University , Canada,” no. 61271212, pp. 811–816, 2017.
G. Q., Huynh-Thu; M., “Scope of validity of PSNR in image/video quality assessment,” Electron. Lett., vol. 44, no. 13, 2008.
B. Nugroho and E. Y. Puspaningrum, “Kinerja Metode CNN untuk Klasifikasi Pneumonia dengan Variasi Ukuran Citra Input,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 3, p. 533, 2021.
B. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017.
N. Fadlia and R. Kosasih, “Klasifikasi Jenis Kendaraan Menggunakan Metode Convolutional Neural Network (Cnn),” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 3, pp. 207–215, 2019.
M. Sara, U., Akter, M., & Uddin, “Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study,” J. Comput. Commun., 2019.
A. Sharma, “Confusion Matrix in Machine Learning,” 2021. https://www.geeksforgeeks.org/confusion-matrix-machine-learning/
P. Mane, Vanita & Jadhav, Suchit & Lal, “Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture,” ITM Web Conf., vol. 32, p. 03044, 2020.
Z. Zhang, S. Shan, Y. Fang, and L. Shao, “Deep Learning for Pattern Recognition,” Pattern Recognit. Lett., vol. 119, pp. 1–2, 2019.
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