Lightweight image super-resolution for IoT devices using deep residual feature distillation network-Reference-Cited by-同舟云学术

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Lightweight image super-resolution for IoT devices using deep residual feature distillation network

Published:2024-02 Issue: Volume:285 Page:111343
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Mardieva Sevara,Ahmad Shabir,Umirzakova Sabina,Rasool M.J. Aashik,Whangbo Taeg Keun

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference50 articles.

1. Image super-resolution using deep convolutional networks;Dong;IEEE Trans. Pattern Anal. Mach. Intell.,2015

2. Image super-resolution using deep convolutional networks;Dong;IEEE Trans. Pattern Anal. Mach. Intell.,2015

3. W. Shi, J. Caballero, F. Huszár, J. Totz, A.P. Aitken, R. Bishop, D. Rueckert, Z. Wang, Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1874–1883.

4. An adaptive approach based on resource-awareness towards power-efficient real-time periodic task modeling on embedded IoT devices;Ahmad;Processes,2018

5. B. Lim, S. Son, H. Kim, S. Nah, K. Mu Lee, Enhanced deep residual networks for single image super-resolution, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 136–144.

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