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6 Deep Learning and RFID System Physical Anti-Collision
4. Hinton GE, Rsalakhutdinov R (2006) Reducing the dimensionality of data with neural networks.
Science 313(5786):504–507
5. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural
Comput 18(7):1527–1554
6. Liu W, Wang Z, Liu X et al (2017) A survey of deep neural network architectures and their
applications. Neurocomputing 234:11–26
7. Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image
analysis. Med Image Anal 42:60–88
8. Faust O, Hagiwara Y, Hong TJ et al (2018) Deep learning for healthcare applications based on
physiological signals: a review. Comput Meth Programs Biomed 161:1–13
9. Lefkimmiatis S (2018) Universal denoising networks: a novel CNN architecture for image
denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp
3204–3213.
10. Park JH, Kim JH, Cho SI (2018) The analysis of CNN structure for image denoising. In:
International SoC Design Conference (ISOCC), pp 220–221
11. Dong C, Loy CC, He KM et al (2016) Image super-resolution using deep convolutional
networks. IEEE T. Pattern Anal 38(2):295–307
12. Davy A, Ehret T, Facciolo G et al, Non-Local Video Denoising by CNN. https://arxiv.org/abs/
1811.12758
13. Yu XL, Wang DH, Zhao ZM (2019) Semi-physical verification technology for dynamic
performance of internet of things system. Springer Singapore.
14. Yu YS, Yu XL, Zhao ZM et al (2018) Image analysis system for optimal geometric distribution
of RFID tags based on flood fill and DLT. IEEE T Instrum Meas 27(4):839–848
15. Tassano M, Delon J, Veit T, An Analysis and Implementation of the FFDNet Image Denoising
Method, Image Processing On Line (IPOL)
16. Zhang K, Zuo WM, Zhang L (2018) FFDNet: Toward a Fast and Flexible Solution for CNNBased Image Denoising. IEEE T Image Process 27(9):4608–4622
17. Gshuhang U et al (2014) Weighted nuclear norm minimization with application to image
denoising. In: Proceedings of the IEEE conference on computer vision and pattern recognition,
pp 2862–2869
18. Zhang K, Zuo WM, Gu SH et al (2017) Learning deep CNN denoiser prior for image restoration.
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3929–3938
19. Zhang K, Zuo WM, Chen YJ et al (2017) Beyond a gaussian denoiser: residual learning of
deep CNN for image denoising. IEEE T Image Process 26(7):3142–3155
20. Dash R, Majhi B (2014) Motion blur parameters estimation for image restoration. Optik - Int
J Light Electron Opt 125(5):1634–1640
21. Wang Z, Yao Z, Wang Q (2017) Improved scheme of estimating motion blur parameters for
image restoration. Digit Signal Prog 65:11–18
22. Takagi Y, Fujisawa T, Ikehara M (2017) Image restoration of JPEG encoded images via
block matching and wiener filtering, IEICE Trans Fundam Electron Commun Comput Sci
100(9):1993–2000
23. Brifman A, Romano Y, Elad M (2016) Turning a denoiser into a super-resolver using plug and
play priors. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE, pp
1404–1408
24. Chan SH, Wang X, Elgendy OA (2016) Plug-and-play ADMM for image restoration: fixedpoint convergence and applications. IEEE Trans Comput Imaging 3(1):84–98
25. Romano Y, Elad M, Milanfar P (2017) The little engine that could: Regularization by denoising
(RED). SIAM J Imaging Sci 10(4):1804–1844
26. Zoran D, Weiss Y (2011) From learning models of natural image patches to whole image
restoration. In: 2011 international conference on computer vision. IEEE, pp 479–486
27. Heide F, Steinberger M, Tsai YT et al (2014) FlexISP: A flexible camera image processing
framework. ACM Trans Graph (TOG) 33(6):231
28. Chambolle A, Pock T (2011) A first-order primal-dual algorithm for convex problems with
applications to imaging. J Math Imaging Vis 40(1):120–145
6 Deep Learning and RFID System Physical Anti-Collision
4. Hinton GE, Rsalakhutdinov R (2006) Reducing the dimensionality of data with neural networks.
Science 313(5786):504–507
5. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural
Comput 18(7):1527–1554
6. Liu W, Wang Z, Liu X et al (2017) A survey of deep neural network architectures and their
applications. Neurocomputing 234:11–26
7. Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image
analysis. Med Image Anal 42:60–88
8. Faust O, Hagiwara Y, Hong TJ et al (2018) Deep learning for healthcare applications based on
physiological signals: a review. Comput Meth Programs Biomed 161:1–13
9. Lefkimmiatis S (2018) Universal denoising networks: a novel CNN architecture for image
denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp
3204–3213.
10. Park JH, Kim JH, Cho SI (2018) The analysis of CNN structure for image denoising. In:
International SoC Design Conference (ISOCC), pp 220–221
11. Dong C, Loy CC, He KM et al (2016) Image super-resolution using deep convolutional
networks. IEEE T. Pattern Anal 38(2):295–307
12. Davy A, Ehret T, Facciolo G et al, Non-Local Video Denoising by CNN. https://arxiv.org/abs/
1811.12758
13. Yu XL, Wang DH, Zhao ZM (2019) Semi-physical verification technology for dynamic
performance of internet of things system. Springer Singapore.
14. Yu YS, Yu XL, Zhao ZM et al (2018) Image analysis system for optimal geometric distribution
of RFID tags based on flood fill and DLT. IEEE T Instrum Meas 27(4):839–848
15. Tassano M, Delon J, Veit T, An Analysis and Implementation of the FFDNet Image Denoising
Method, Image Processing On Line (IPOL)
16. Zhang K, Zuo WM, Zhang L (2018) FFDNet: Toward a Fast and Flexible Solution for CNNBased Image Denoising. IEEE T Image Process 27(9):4608–4622
17. Gshuhang U et al (2014) Weighted nuclear norm minimization with application to image
denoising. In: Proceedings of the IEEE conference on computer vision and pattern recognition,
pp 2862–2869
18. Zhang K, Zuo WM, Gu SH et al (2017) Learning deep CNN denoiser prior for image restoration.
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3929–3938
19. Zhang K, Zuo WM, Chen YJ et al (2017) Beyond a gaussian denoiser: residual learning of
deep CNN for image denoising. IEEE T Image Process 26(7):3142–3155
20. Dash R, Majhi B (2014) Motion blur parameters estimation for image restoration. Optik - Int
J Light Electron Opt 125(5):1634–1640
21. Wang Z, Yao Z, Wang Q (2017) Improved scheme of estimating motion blur parameters for
image restoration. Digit Signal Prog 65:11–18
22. Takagi Y, Fujisawa T, Ikehara M (2017) Image restoration of JPEG encoded images via
block matching and wiener filtering, IEICE Trans Fundam Electron Commun Comput Sci
100(9):1993–2000
23. Brifman A, Romano Y, Elad M (2016) Turning a denoiser into a super-resolver using plug and
play priors. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE, pp
1404–1408
24. Chan SH, Wang X, Elgendy OA (2016) Plug-and-play ADMM for image restoration: fixedpoint convergence and applications. IEEE Trans Comput Imaging 3(1):84–98
25. Romano Y, Elad M, Milanfar P (2017) The little engine that could: Regularization by denoising
(RED). SIAM J Imaging Sci 10(4):1804–1844
26. Zoran D, Weiss Y (2011) From learning models of natural image patches to whole image
restoration. In: 2011 international conference on computer vision. IEEE, pp 479–486
27. Heide F, Steinberger M, Tsai YT et al (2014) FlexISP: A flexible camera image processing
framework. ACM Trans Graph (TOG) 33(6):231
28. Chambolle A, Pock T (2011) A first-order primal-dual algorithm for convex problems with
applications to imaging. J Math Imaging Vis 40(1):120–145