PERBAIKAN KUALITAS CITRA BAWAH AIR DENGAN ALGORITMA MSRCR (MULTISCALE RETINEX WITH COLOR RESTORATION)
Abstract
The underwater environment has high complexity, which naturally arises due to the absorption and scattering of light waves by water and other particles suspended in it. This results in underwater images with low contrast, blurriness, and unclear details, necessitating the use of image enhancement methods. The Retinex method aims to maintain color constancy, where the color of an object appears relatively the same even under different lighting conditions. Preliminary research indicates that the SSR and MSR methods still produce halo effects. Therefore, this study modifies and enhances the MSR method by adding color restoration to correct the color distortion caused by water. This research begins with test images of 5 x 5 dimensions. The image enhancement process is conducted using the proposed MSRCR method and then analyzed mathematically on digital images. Subsequently, it is applied to underwater images with different sigma (
Full Text:
PDFReferences
Z. Huang, J. Li, and Z. Hua, “Attention-based for Multiscale Fusion Underwater Image Enhancement.,” KSII Transactions on Internet & …, 2022.
H. Tang, H. Zhu, L. Fei, T. Wang, Y. Cao, and C. Xie, “Low-Illumination Image Enhancement Based on Deep Learning Techniques: A Brief Review,” Photonics, vol. 10, no. 2, 2023, doi: 10.3390/photonics10020198.
M. S. Ahmed, T. T. Aurpa, and M. A. K. Azad, “Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, pp. 5170–5182, 2022, doi: 10.1016/j.jksuci.2021.05.003.
J. Zhou, D. Zhang, and W. Zhang, “Underwater image enhancement method via multi-feature prior fusion,” Applied Intelligence, 2022, doi: 10.1007/s10489-022-03275-z.
K. A. Islam, “Deep Learning Approaches for Seagrass Detection in Deep Learning Approaches for Seagrass Detection in Multispectral Imagery Multispectral Imagery,” 2021, doi: 10.25777/gct9-yr76.
C. Fabbri, M. J. Islam, and J. Sattar, “Enhancing underwater imagery using generative adversarial networks,” 2018 IEEE International …, 2018, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8460552/
C. O. Ancuti, “Color Balance and Fusion for Underwater Image Enhancement,” IEEE Transactions on Image Processing, vol. 27, no. 1, pp. 379–393, 2018, doi: 10.1109/TIP.2017.2759252.
D. G. Kim and S. M. Kim, “Single image-based enhancement techniques for underwater optical imaging,” Journal of Ocean Engineering and Technology, 2020, [Online]. Available: https://www.koreascience.or.kr/article/JAKO202007159775134.page
F. Tian, T. Chen, and J. Zhang, “Research on Improved Retinex-Based Image Enhancement Method for Mine Monitoring,” Applied Sciences (Switzerland), vol. 13, no. 4, 2023, doi: 10.3390/app13042672.
L. Dong, L. Zhao, and J. Wang, “Image enhancement via texture protection Retinex,” IET Image Process, vol. 16, no. 1, pp. 61–78, 2022, doi: 10.1049/ipr2.12311.
B. Liang, X. X. Jia, and Y. Lu, “Application of Adaptive Image Restoration Algorithm Based on Sparsity of Block Structure in Environmental Art Design,” Complexity, vol. 2021, 2021, doi: 10.1155/2021/9035163.
R. S. Cruz, L. Lebrat, P. Bourgeat, C. Fookes, J. Fripp, and O. Salvado, “DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction,” 2021. [Online]. Available: http://maxwellplus.com/
DOI: https://doi.org/10.33365/jti.v18i2.4222
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Sri Dianing Asri
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
JURNAL TEKNOINFO
Published by Universitas Teknokrat Indonesia
Organized by Prodi S1 Informatika FTIK Universitas Teknokrat Indonesia
W: http://ejurnal.teknokrat.ac.id/index.php/teknoinfo/index
E : teknoinfo@teknokrat.ac.id.
Jl. Zainal Abidin Pagaralam, No.9-11, Labuhan Ratu, Bandarlampung
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Jumlah Pengunjung : View Teknoinfo StatsCounter