IMPLEMENTASI RESTORASI CITRA DERAU SALT & PEPPER, GAUSSIAN DAN SPECKLE SECARA SPASIAL DENGAN MATLAB
Abstract
Citra yang mengandung derau seringkali membatasi informasi berharga yang dibutuhkan untuk analisis citra. Restorasi citra mengacu pada pengapusan atau pengurangan degradasi citra yang dihasilkan dari proses pengambilan data atau proses akuisisi citra. Degradasi yang dimaksud meliputi derau error atau efek optik misalnya blur karena kamera yang tidak fokus atau karena goyangan kamera. Untuk menanggulangi hal tersebut, pada penelitian ini diimplementasikan restorasi citra dengan teknik secara spasial pada citra yang mengalami kerusakan akibat derau salt & pepper, derau gaussian dan derau speckle. Dari implementasi restotasi citra dan analisis pengujian MSE dan PSNR, citra derau gausssian dapat direstotasi filter median 5×5 dengan baik dan maksimal ditunjukkan dengan MSE 58,9 dan PSNR 101,23 sedangkan citra derau speckel kurang dapat direstorasi dengan filter rata-rata 3×3 yaitu dengan MSE 191,42 dan PSNR 80,75.
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References
Chakole Vijay V, (2012). "Digital Image Processing, Unit. 6 Image restoration and reconstruction". Department of Electronics Engineering, KDKCE, Nagpur.
Dai Jingjing, Au Oscar C, Fellow, IEEE, Fang Lu, Pang Chao, Zou Feng, and Li Jiali, (2013). "Multichannel Non-Local Means Fusion for Color Image Denoising". Journal of IEEE
Gilby Ben L, Olds Andrew D, Connolly Rod M, Henderson Christopher J, And Schlacher Thomas A, (2018). "Spatial Restoration Ecology: Placing Restoration in a Landscape Context". BioScience, December 2018, Vol. 68 No. 12, Hal: 1007-1019.
Gonzalez RC and Rafael EW, (2008), “Digital Image Processing”, Prentice–Hall, Inc. United State, America.
Gunturk Bahadir K. dan Li Xin, (2013). "Image Restoration - Fundamentals and Advances". CRC Press. at Boca Raton-New York, London
Hua, Tao; et al (2011). "Evaluation of the quality of a speckle pattern in the digital image correlation method by mean subset fluctuation". Optics & Laser Technology. 43 (1): 9–13.
Jaina Paras dan Tyagi Vipin, (2014). "Spatial and Frequency Domain Filters for Restoration of Noisy Images", IETE Journal Of Education, Vol 54 - Issue 2. at UNAM Ciudad Universitaria.
Niknejad Milad, Rabbani Hossein and Babaie-Zadeh Massoud, (2015). Image Restoration Using Gaussian Mixture Models With Spatially Constrained Patch Clusterin. Journal Of IEEE Transactions on Image Processing, Vol. Xx, No. X, 201x :1057-7149.
O.S. Faragallah et al, (2021). "A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions". Digital Object Identifier. IEEE Acces - 10.1109/.3048315.
Rosin, Paul; Collomosse, John (2012). Image and Video-Based Artistic Stylisation. Springer Publishing. p. 92. ISBN 9781447145196.
Sharma Anmol and Singh Jagroop,(2013). Image Denoising using Spatial Domain Filters: A Quantitative Study. 6th International Congress on Image and Signal Processing (CISP), 293-298.
Talebi Hossein and Milanfa Peyman, (2014)."Global Denoising Is Asymptotically Optimal. International Conference on Image Processing. (IEEE ICIP).
Zhang Jian, Zhao Debin, Xiong Ruiqin, Ma Siwei, and Gao Wen, (2014). Image Restoration Using Joint Statistical Modeling in a Space-Transform Domain. IEEE Transactions On Circuits And Systems For Video Technology, VOL. 24, NO. 6: 915-928.