RAQAMLI TASVIRLARNI QAYTA ISHLASHDA SHOVQINNI KAMAYTIRISH USULLAR
Keywords:
Kalit so`zlar: Raqamli tasvir, shovqini kamaytirish, filtrlar, transformatsion metodlar, adaptiv filtrlar, chuqur o‘rganish, konvolyutsion neyron tarmoqlari (CNN), PSNR, SSIM.Abstract
Annotatsiya
Raqamli tasvirlarda shovqin paydo bo‘lishi tasvir sifatini pasaytiradi va turli
sohalarda, jumladan tibbiyot, sanoat, sun’iy intellekt va kompyuter grafikasi
loyihalarida natijaviy ishlashga salbiy ta’sir ko‘rsatadi. Shovqinni kamaytirish turli
yondashuvlar orqali amalga oshiriladi, jumladan an’anaviy lineer va no-lineer filtrlar,
transformatsion metodlar, statistik adaptiv usullar hamda mashina o‘rganish va chuqur
o‘rganish algoritmlari. Natijalar shuni ko‘rsatadiki, har bir yondashuvning o‘ziga xos
afzallik va cheklovlari mavjud bo‘lib, ularni vaziyatga moslashtirish orqali tasvir
sifatini maksimal darajada yaxshilash mumkin. Turli usullarni solishtirish va PSNR
hamda SSIM kabi metrikalar asosida baholash ularning samaradorligini raqamli
jihatdan aniqlash imkonini beradi. Shu bilan birga, maqola natijalari turli sohalarda
amaliy qo‘llanish imkoniyatlarini ko‘rsatadi va kelajakda yanada samarali shovqinni
kamaytirish algoritmlarini ishlab chiqish uchun asos yaratadi [1–9].
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