Comparison of Noise Using Reduction Method for Repairing Digital Image

Amin Padmo Azam Masa - Mulawarman University, Samarinda, Indonesia
Muhamad Mushfa Hikmatal Fajri - Mulawarman University, Samarinda, Indonesia
Anindita Septiarini - Mulawarman University, Samarinda, Indonesia
Edy Winarno - Stikubank University, Semarang, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.4.2032

Abstract


Digital images are used to become a visual bridge of information. The information data must be precise so that the information can be adequately conveyed, but in the process, digital images sometimes experience a change in quality. One of the causes of this change is noise, where the image affected by noise is of poor quality, so misinformation can occur. This problem can be solved using filtering methods, but there are so many filtering methods. In this study, five filtering methods were used, including the Gaussian filter, mean filter, median filter, wiener filter, and conservative filter, to be compared with two types of noise, such as salt and pepper and speckle, so that the best method for noise reduction in digital images is known based on the criteria that have been set determined. The research results were determined based on the value of the measurement parameters Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The results show that the conservative method is the best based on the parameter values of MSE 3.21 and PSNR = 37.99. However, when viewed visually, the median method is superior for reducing noise in digital images that have been carried out. The results of the research can be used as information to develop future research, especially in the field of digital image processing.


Keywords


Image filtering; noise; MSE; PSNR

Full Text:

PDF

References


J. Cao, P. Qi, Q. Sheng, T. Yang, J. Guo, and J. Li, “Exploring the role of visual content in fake news detection,” Disinformation, Misinformation, Fake News Soc. Media Emerg. Res. Challenges Oppor., pp. 141–161, 2020.

M. C. Roco, “Principles of convergence in nature and society and their application: from nanoscale, digits, and logic steps to global progress,” J. Nanoparticle Res., vol. 22, no. 11, p. 321, 2020.

A. Saranya, K. Kottursamy, A. A. AlZubi, and A. K. Bashir, “Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation,” Soft Comput., pp. 1–15, 2021.

N. Kheradmandi and V. Mehranfar, “A critical review and comparative study on image segmentation-based techniques for pavement crack detection,” Constr. Build. Mater., vol. 321, p. 126162, 2022.

J. Azzeh, B. Zahran, and Z. Alqadi, “Salt and pepper noise: Effects and removal,” JOIV Int. J. Informatics Vis., vol. 2, no. 4, pp. 252–256, 2018.

B. Garg, “Restoration of highly salt-and-pepper-noise-corrupted images using novel adaptive trimmed median filter,” Signal, Image Video Process., vol. 14, no. 8, pp. 1555–1563, 2020.

K. Khan et al., “Recent progress, challenges, and prospects in two-dimensional photo-catalyst materials and environmental remediation,” Nano-Micro Lett., vol. 12, pp. 1–77, 2020.

I. Kim et al., “Nanophotonics for light detection and ranging technology,” Nat. Nanotechnol., vol. 16, no. 5, pp. 508–524, 2021.

Z. Du, J. Yuan, F. Xiao, and C. Hettiarachchi, “Application of image technology on pavement distress detection: A review,” Measurement, vol. 184, p. 109900, 2021.

Y. Su and Q. Zhang, “Glare: A free and open-source software for generation and assessment of digital speckle pattern,” Opt. Lasers Eng., vol. 148, p. 106766, 2022.

A. M. Cheema, S. M. Adnan, and Z. Mehmood, “A novel optimized semi-blind scheme for color image watermarking,” IEEE Access, vol. 8, pp. 169525–169547, 2020.

R. L. Motornyuk and S. M. Bilan, “The moving object detection and research effects of noise on images based on cellular automata with a hexagonal coating form and radon transform,” in Handbook of Research on Intelligent Data Processing and Information Security Systems, IGI Global, 2020, pp. 330–359.

F. Abdullakutty, E. Elyan, and P. Johnston, “A review of state-of-the-art in Face Presentation Attack Detection: From early development to advanced deep learning and multi-modal fusion methods,” Inf. fusion, vol. 75, pp. 55–69, 2021.

S. Yoon et al., “Deep optical imaging within complex scattering media,” Nat. Rev. Phys., vol. 2, no. 3, pp. 141–158, 2020.

W. Lu, Y. Xue, Y. Yeung, H. Liu, J. Huang, and Y.-Q. Shi, “Secure halftone image steganography based on pixel density transition,” IEEE Trans. Dependable Secur. Comput., vol. 18, no. 3, pp. 1137–1149, 2019.

E. Solovyeva and A. Abdullah, “Dual Autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images,” J. Imaging, vol. 8, no. 9, p. 250, 2022.

F. Huettig, J. Audring, and R. Jackendoff, “A parallel architecture perspective on pre-activation and prediction in language processing,” Cognition, vol. 224, p. 105050, 2022.

Z. A. Abduljabbar et al., “Provably secure and fast color image encryption algorithm based on s-boxes and hyperchaotic map,” IEEE Access, vol. 10, pp. 26257–26270, 2022.

A. Maity, A. Pattanaik, S. Sagnika, and S. Pani, “A Comparative Study on Approaches to Speckle Noise Reduction in Images,” Int. Conf. Comput. Intell. Networks, pp. 148–155, 2015, doi: 10.1109/CINE.2015.36.

H. Studiawan, G. Grispos, and K.-K. R. Choo, “Unmanned Aerial Vehicle (UAV) Forensics: The Good, The Bad, and the Unaddressed,” Comput. Secur., p. 103340, 2023.

L. Jiao and J. Zhao, “A survey on the new generation of deep learning in image processing,” Ieee Access, vol. 7, pp. 172231–172263, 2019.

S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 7, pp. 3523–3542, 2021.

H. Dong et al., “Deep Shearlet Network for Change Detection in SAR Images,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–15, 2022.

R. Chen, Z. Zheng, Y. Yu, H. Zhao, J. Ren, and H.-Z. Tan, “Fast restoration for out-of-focus blurred images of qr code with edge prior information via image sensing,” IEEE Sens. J., vol. 21, no. 16, pp. 18222–18236, 2021.

X. Liao, K. Li, X. Zhu, and K. J. R. Liu, “Robust detection of image operator chain with two-stream convolutional neural network,” IEEE J. Sel. Top. Signal Process., vol. 14, no. 5, pp. 955–968, 2020.

G. Zhou, W. Zhang, A. Chen, M. He, and X. Ma, “Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion,” IEEE access, vol. 7, pp. 143190–143206, 2019.

X. Pan, X. Zhan, B. Dai, D. Lin, C. C. Loy, and P. Luo, “Exploiting deep generative prior for versatile image restoration and manipulation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 11, pp. 7474–7489, 2021.

J. Chen, Y. Zhan, and H. Cao, “Adaptive sequentially weighted median filter for image highly corrupted by impulse noise,” IEEE Access, vol. 7, pp. 158545–158556, 2019.

M. Shi, F. Zhang, S. Wang, C. Zhang, and X. Li, “Detail preserving image denoising with patch-based structure similarity via sparse representation and SVD,” Comput. Vis. Image Underst., vol. 206, p. 103173, 2021.

S. Yasser, A. Hesham, M. Hassan, and W. Alexan, “AES-secured bit-cycling steganography in sliced 3d images,” in 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), IEEE, 2020, pp. 227–231.

M. T. Elkandoz, W. Alexan, and H. H. Hussein, “Double-layer image security scheme with aggregated mathematical sequences,” in 2019 international conference on advanced communication technologies and networking (CommNet), IEEE, 2019, pp. 1–7.