REAL VAQT REJIMIDA VIDEOMAʼLUMOTLARNI TIKLASH VA SIFATINI OSHIRISH UCHUN CHUQUR OʼRGANISH METODLARI
Keywords:
real vaqt video, chuqur o‘rganish, CNN, GAN, video tiklash, super-resolution, shovqinni kamaytirish.Abstract
Ushbu maqolada real vaqt rejimida videomaʼlumotlarni tiklash va sifatini oshirish masalalari hamda ularni hal etishda chuqur o‘rganish metodlaridan foydalanish imkoniyatlari tahlil qilinadi. Video oqimlarda uchraydigan shovqin, past aniqlik, harakatdagi xiralashuv va siqish artefaktlari kabi muammolarni bartaraf etishda konvolyutsion neyron tarmoqlar (CNN), rekurrent neyron tarmoqlar (RNN), generativ qarama-qarshi tarmoqlar (GAN) va transformer arxitekturalariga asoslangan yondashuvlar ko‘rib chiqiladi. Shuningdek, real vaqt talablarini qondirish uchun hisoblash murakkabligini kamaytirish va optimallashtirish masalalariga alohida e’tibor qaratiladi.
References
1. Goodfellow I., Bengio Y., Courville A. Deep Learning. - Cambridge: MIT Press, 2016.
2. LeCun Y., Bengio Y., Hinton G. Deep learning // Nature. - 2015. - Vol. 521. - P. 436–444.
3. Ledig C. et al. Photo-realistic single image super-resolution using a generative adversarial network // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). - 2017. - P. 4681–4690.
4. 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(7). - P. 3142–3155.
5. Wang X., Chan K. C. K., Yu K., Dong C., Loy C. C. EDVR: Video restoration with enhanced deformable convolutional networks // CVPR Workshops. - 2019.
6. Tao X., Gao H., Shen X., Wang J., Jia J. Scale-recurrent network for deep image deblurring // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. - 2018.
7. Lim B., Son S., Kim H., Nah S., Mu Lee K. Enhanced deep residual networks for single image super-resolution // CVPR Workshops. - 2017.
8. Caballero J. et al. Real-time video super-resolution with spatio-temporal networks and motion compensation // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. - 2017.
9. Shi W., Caballero J., Huszár F. et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network // Proceedings of CVPR. - 2016.
10. Szeliski R. Computer Vision: Algorithms and Applications. - London: Springer, 2022.
11. Gonzalez R. C., Woods R. E. Digital Image Processing. - 4th ed. - Pearson, 2018.
12. Wang Z., Bovik A. C., Sheikh H. R., Simoncelli E. P. Image quality assessment: From error visibility to structural similarity // IEEE Transactions on Image Processing. - 2004. - Vol. 13(4). - P. 600–612.
