RAQAMLI TASVIRLARNI SHOVQINDAN TOZALASHDA FILTRLAR SAMARADORLIGINI TAQQOSLASH

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Abstrak:

Zamonaviy dunyoda raqamli tasvirlar tibbiyot, xavfsizlik, telekommunikatsiya va boshqa sohalarda keng qo'llanilmoqda. Ammo tasvirlar ko'pincha turli shovqinlar ta'siriga uchraydi va bu ularning sifatini pasaytiradi. Ushbu tadqiqotda tasvirlarni shovqindan tozalashning to'rt xil usuli - Gauss, Median, Bilateral va Wavelet filtrlari - taqqoslandi. Natijalar shuni ko'rsatdiki, har bir filtr ma'lum shovqin turiga mos keladi va universal yechim mavjud emas. Gauss shovqini uchun Bilateral filtri eng yuqori PSNR ko'rsatkichini (29.67 dB) ko'rsatdi, Salt & Pepper shovqini uchun esa Median filtri 32.67 dB bilan eng samarali bo'ldi.

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##submission.citations##:

Gonzalez R.C., Woods R.E. Digital Image Processing. 4th Edition. Pearson, 2018. 1168 p.

Buades A., Coll B., Morel J.M. A non-local algorithm for image denoising // IEEE Computer Vision and Pattern Recognition. 2005. Vol. 2. P. 60-65.

Tomasi C., Manduchi R. Bilateral filtering for gray and color images // Sixth International Conference on Computer Vision. 1998. P. 839-846.

O'zbekiston Respublikasining "Axborot texnologiyalari va kommunikatsiyalar to'g'risida"gi Qonuni. Toshkent, 2020.

Zhang K., Zuo W., Chen Y., Meng D., Zhang L. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising // IEEE Transactions on Image Processing. 2017. Vol. 26. No. 7. P. 3142-3155.

Dabov K., Foi A., Katkovnik V., Egiazarian K. Image denoising by sparse 3-D transform-domain collaborative filtering // IEEE Transactions on Image Processing. 2007. Vol. 16. No. 8. P. 2080-2095.