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Image enhancement is becoming increasingly im portant with the advancement of space exploration techniques and the technological development of more durable and sci entifically sound observatories equipped with more powerful telescopes. The enhancement of images helps astronomers an alyze the results and act toward determining the dates of religious festivals. This work describes a technique known as contrast-limited adaptive histogram equalization (CLAHE) with grayscale contrast enhancement and bilateral filtering. We apply CLAHE on the L component of the CIE-Lab color space to adjust lightness contrast. Subsequently, grayscale contrast enhancement is performed to increase the visibility of the moon crescent. Noise caused by grayscale contrast en hancement is reduced using bilateral filtering. Two quantitative measures are selected (PSNR and MSE) to show the visual improvement achieved by the proposed algorithm.
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3--13
Opis fizyczny
Bibliogr. 38 poz., rys., tab.
Twórcy
- Intelligent Informatics, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu
autor
- Intelligent Informatics, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu
autor
- East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, 21300 Kuala Terengganu, Terengganu, Malaysia
- East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, 21300 Kuala Terengganu, Terengganu, Malaysia
- Intelligent Informatics, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu
autor
- Department of Physics, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Federal Territory, Malaysia
Bibliografia
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- [23] M. Fakhar, P. Moalem, and M. A. Badri, „Lunar crescent detection based on image processing algorithms", Earth, Moon, and Planets, vol. 114, no. 1, pp. 17-34, 2014 (DOI: 10.1007/s11038-014-9449-3).
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- [25] M. Kaur, J. Kaur, and J. Kaur, „Survey of contrast enhancement techniques based on histogram equalization", Int. J. of Adv. Computer Sci. and App., vol. 2, no. 7, 2011 (DOI: 10.14569/IJACSA.2011.020721).
- [26] D. J. Ketcham, „Real-time image enhancement techniques", in Image Processing, vol. 74, pp. 120-125, 1976 (DOI: 10.1117/12.954708).
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- [30] T. Ayyavoo and J. J. Suseela, „Illumination pre-processing metod for face recognition using 2D DWT and CLAHE", IET Biometrics, vol. 7, no. 4, pp. 380-390, 2017 (DOI:10.1049/iet-bmt.2016.0092).
- [31] L. Li, Y. Si, and Z. Jia, „Medical image enhancement based on CLAHE and unsharp masking in NSCT domain", J. of Medical Imaging and Health Informatics, vol. 8, no. 3, pp. 431-438, 2018 (DOI: 10.1166/jmihi.2018.2328).
- [32] J. Ma, X. Fan, S. X. Yang, X. Zhang, and X. Zhu, „Contrast limited adaptive histogram equalization-based fusion in YIQ and HSI colour spaces for underwater image enhancement", Int. J. of Pattern Recognition and Arti_cial Intell., vol. 32, no. 7, 2018 (DOI: 10.20944/preprints201703.0086.v1).
- [33] S. Sahu, A. K. Singh, S. P. Ghrera, and M. Elhoseny, „An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE", Optics & Laser Technol., vol. 110, pp. 87-98, 2019 (DOI: 10.1016/j.optlastec.2018.06.061).
- [34] M. Siddhartha and A. Santra, „COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19", ArXiv, 2020 [Online]. Available: https://arxiv.org/pdf/2006.13873.pdf
- [35] R. M. James and A. Sunyoto, „Detection Of CT-Scan Lungs COVID-19 Image Using Convolutional Neural Network And CLAHE", In Proc. IEEE 3rd Int. Conf. on Informat. and Commun. Technol. (ICOIACT), Yogyakarta, Indonesia, 2020, pp. 302-307 (DOI: 10.1109/ICOIACT50329.2020.9332069).
- [36] B. K. Umri, M. W. Akhyari, and K. Kusrini, „Detection of Covid-19 in Chest X-ray Image using CLAHE and Convolutional Neural Network, in 2nd IEEE Int. Conf. on Cybernet. and Intelligent System (ICORIS), Manado, Indonesia, 2020, pp. 1-5 (DOI: 10.1109/ICORIS50180.2020.9320806).
- [37] G. Siracusano et al., „Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE)", Sustainability, vol. 12, no. 20, 2020 (DOI: 10.3390/su12208573).
- [38] C. Tomasi and R. Manduchi, „Bilateral filtering for gray and color images", in Sixth Int. Conf. on Computer Vision (IEEE Cat. No. 98CH36271), Bombay, India, 1998, pp. 839-846 (DOI: 10.1109/ICCV.1998.710815).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6406f0cd-644e-4472-922a-247d983aed46
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