Noise robust face super-resolution via learning of spatial attentive features-Reference-Cited by-同舟云学术

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Noise robust face super-resolution via learning of spatial attentive features

Published:2023-02-21 Issue:16 Volume:82 Page:25449-25465
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Tomar Anurag Singh^ORCID,Arya K. V.^ORCID,Rajput Shyam Singh^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-14472-4.pdf

Reference53 articles.

1. Ajagbe SA, Amuda KA, Oladipupo MA, Oluwaseyi FA, Okesola KI (2021) Multi-classification of alzheimer disease on magnetic resonance images (mri) using deep convolutional neural network (dcnn) approaches. Int J Adv Comput Res 11(53):51

2. Al-Obaydy WNI, Suandi SA (2020) Automatic pose normalization for open-set single-sample face recognition in video surveillance. Multimed Tools Appl 79(3):2897–2915

3. Baker S, Kanade T (2000) Hallucinating faces. In: Proceedings Fourth IEEE international conference on automatic face and gesture recognition (Cat. No. PR00580). IEEE, pp 83–88

4. Bao Q, Gang B, Yang W, Zhou J, Liao Q (2022) Attention-driven graph neural network for deep face super-resolution. IEEE Trans Image Process 31:6455–6470

5. Bulat A, Tzimiropoulos G (2017) How far are we from solving the 2d & 3d face alignment problem?(and a dataset of 230,000 3d facial landmarks). In: Proceedings of the IEEE international conference on computer vision, pp 1021–1030

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