Raqamli tasvirlarda shovqinni filtrlash usullarining samaradorligini taqqoslash
##semicolon##
Raqamli tasvir##common.commaListSeparator## shovqin##common.commaListSeparator## tasvirni filtrlash##common.commaListSeparator## median filtr##common.commaListSeparator## Gauss filtri##common.commaListSeparator## tasvir sifatiAnnotatsiya
Ushbu maqolada raqamli tasvirlarda uchraydigan shovqin turlari hamda ularni kamaytirish uchun qo‘llaniladigan filtrlash usullarining samaradorligi tahlil qilinadi. Asosan chiziqli va nochiziqli filtrlash usullari – o‘rtacha (mean), median, Gauss, Wiener va bilateral filtrlardan foydalanish orqali tasvir sifatini yaxshilash masalalari ko‘rib chiqilgan. Har bir filtrning afzallik va kamchiliklari vizual hamda statistik ko‘rsatkichlar asosida solishtiriladi. Tadqiqot natijalari shuni ko‘rsatadiki, shovqin turiga mos filtrni tanlash tasvir aniqligi va axborot saqlanishiga bevosita ta’sir ko‘rsatadi
##submission.citations##
1. Gonzalez R. C., Woods R. E. Digital Image Processing. – 4th Edition. Pearson Education, 2018.
2. Jain A. K. Fundamentals of Digital Image Processing. – Prentice Hall, 1989.
3. Pratt W. K. Digital Image Processing: PIKS Scientific Inside. – Wiley-Interscience, 2007.
4. Lim J. S. Two-Dimensional Signal and Image Processing. – Prentice Hall, 1990.
5. Bovik A. C. (Ed.). Handbook of Image and Video Processing. – Academic Press, 2010.
6. Tomasi C., Manduchi R. Bilateral Filtering for Gray and Color Images // Proceedings of the IEEE International Conference on Computer Vision, 1998. – pp. 839–846.
7. Wiener N. Extrapolation, Interpolation, and Smoothing of Stationary Time Series. – MIT Press, 1949.
8. Buades A., Coll B., Morel J. M. A Non-Local Algorithm for Image Denoising // IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
9. 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.
10. OpenCV Documentation. Image Filtering Techniques.
