SIGNAL VA TASVIRLARNI SHOVQINDAN TOZALASHDA MASHINAVIY O‘RGANISH USULLARI

Article Sidebar

PDF (Русский)
Issue: Vol. 3 No. 53 : Young scientists
Section: Maqolalar
Published:

Main Article Content

Universitet: Farg’ona Davlat Texnika Universiteti
alisherbekdehqonov43@gmail.com
Universitet: Farg’ona Davlat Texnika Universiteti
aminjonovnurulloh25@gmail.com

Abstract:

Ushbu maqolada signal va tasvirlarni shovqindan tozalashga qaratilgan mashinaviy o‘rganish usullari o‘rganildi. Konvolyutsion neyron tarmoqlar (CNN), avtrokodlovchi (Autoencoder), rekurrent neyron tarmoqlar (RNN, LSTM) va transformer arxitekturasi asosida qurilgan modellar tahlil qilindi. Har bir yondashuvning afzalliklari, cheklovlari va amaliy qo‘llanish sohalari ko‘rsatildi. Shuningdek, turli modellarning tasvir va audio signalni tozalashdagi samaradorligi bo‘yicha tajriba natijalari taqdim etildi.

Article Details

How to Cite:

Dexqonov , A., & Aminjonov , N. (2025). SIGNAL VA TASVIRLARNI SHOVQINDAN TOZALASHDA MASHINAVIY O‘RGANISH USULLARI. Young Scientists, 3(53), 99–101. Retrieved from https://in-academy.uz/index.php/yo/article/view/69008

References:

Goodfellow I., Bengio Y., Courville A. Deep Learning, MIT Press, 2016.

Ronneberger O. U-Net Architecture, MICCAI, 2015.

Zhang K. DnCNN: Beyond Gaussian Denoiser, IEEE CVPR, 2017.

Vaswani A. Attention Is All You Need, NIPS, 2017.

Pascual S. SEGAN: Speech Enhancement GAN, INTERSPEECH, 2017.

Luo Y. & Mesgarani N. Conv-TasNet, ICASSP, 2019.