Proposed method for digital image normalisation

Omar Muayad Abdullah

Abstract


Image normalisation is considered as an important factor in the scope of image enhancement. In this research paper we introduced a proposed model used for image normalisation (contrast stretching) through two phases, design phase and implementation phase. First, the design phase consists of the proposed formulas used for processing the degraded images, where the first formula represents the processing of the darked image illuminations and the second one represents the processing of the highlighted image illuminations, the second part of the design phase we determined which formula has to be used for processing the image degradation. So here for processing this part, we used a K-means clustering machine learning algorithm. The second part is the implementation phase which is used for applying the proposed model and the final step comparing the obtained results with other determined normalisation algorithms.

Keywords


Normalisation, K-means Clustering, ML algorithm

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References


Rangayya, Virupakshappa, and N. Patil, Facial Image Segmentation by Integration of Level Set and Neural Network Optimization with Hybrid Filter Pre-processing Model, Eng. Sci., vol. 16, no. I, pp. 211-220, 2021, DOI:10.30919/es8d583.

Kristina P. Sinaga, Miin-Shen Yang, Unsupervised K-means Clustering Algorithm, IEEE Access Multidisciplinary open access journal, vol.8, pp.80716-80726, 2020, DOI:10.1109/ACCESS.2020.2988796.

Nur Salsabilla, Achmad Fauzi, I Gusti Prahmana, Improving the Quality of Digital Images on Identity Cards Using Contrast Stretching and Retinex Methods, Journal of Artificial Intelligence and Engineering Applications (JAIEA), vol.4, no.1, E-ISSN: 2808-4519, pp. 405-410, 2024.

Wahyu Wijaya Widiyanto, Kusrini, Hanif Al Fatta, Searching Similarity Digital Image using Color Histogram, Techno, vol. 20, no. 1, E-ISSN: 2579-9096,pp. 53-64, 2019.

Erwin, Dwi Ratna Ningsih, Improving Retinal Image Quality using the Contrast Stretching, Histogram Equalization, and CLAHEA Methods with Median Filters, International Journal of Image, Graphics and Signal Processing, pp. 30-41, 2020, DOI: 10.5815/ijigsp.2020.02.04 .

Murinto, Sri Winiarti, Dewi Pramudi Ismi, Adhi Prahara, Image Enhancement using Piecewise Linear Contrast Stretch Methods based on Unsharp Masking Algorithms for leather Image Processing, conference paper ICSITech, pp.115-119, 2017, DOI: 10.1109/ICSITech.2017.8257197.

P. Shamsolmoali, X. Li, R. Wang, Single image resolution enhancement by efficient dilated densely connected residual network, signal process., Image Commun, vol. 79, pp. 13-23, 2019.

B. Alhassan, M., Bagiwa, A. F. D. Kana and M. Abdullahi, A survey of Image Denoising Filters Based on Boundary Discrimination Noise Detection, Fudma J. SCI., vol. 5, no. 4, pp. 12-21, 2022, DOI: 10.33003/fjs-2021-0504-613.

Cai, Z., Chen, J., Deep Least-Squares Methods: An Unsupervised Computational Physics, vol.10, no.16, pp. 1-20, 2019.

C.S.K. Abdullah et al., Review Study of Image Denoising on Digital Image Processing and Applications, J. Adv. Res. Appl. Sci. Eng. Technol., vol.30, no. 1, pp. 331-343, 2023, DOI: 10.37934/araset.30.1.331343.

Manish Suyal, Sanjay Sharma, A Review on Analysis of K -Means Clustering Machine Learning Algorithm based on Unsupervised Learning, Journal of Artificial Intelligence and Systems, vol.6,pp. 85-95, 2024, DOI: 10.33969/AIS.2024060106.

Abiodum M. Ikotun, Absalom E. Ezugwu, et al., K-means clustering algorithms: A comprehansive review, variants analysis, and advances in the era of big data, Information Sciences, vol.622, pp.178-210, 2023, DOI: 10.1016/j.ins.2022.11.139.

Taher M. Ghazal, Muhammad Zahid Hussain, et al., Performance of K-Means Clustering Algorithm with different Distance Metrics, Intelligent Automation & Soft Computing (IASC), vol. 30, no.2, pp.735-742, 2022, DOI:10.32604/iasc.2021.019067.

Abdullah, O. M. (2021, February). Using Fuzzy Inference System FIS for Identifying Motion in Digital Surveillance Systems. In IOP Series: Materials Science and Engineering (Vol. 1094, No. 1, p. 012082).‏

Alessandro Artusi, Francesco Banterle, Fabio Carra, Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics, IEEE Transactions on Image Processing, vol.29, pp.1843-1855, 2020, DOI: 10.1109/TIP.2019.2944079.




DOI: https://doi.org/10.33387/ijeeic.v2i2.10096

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