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Erschienen in:
Weight Factor Determination of Reverse Distance Weighting Method in Computation of Geomagnetic Diurnal Variation Data Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

Application of Improved RBF Neural Network in Remote Sensing Image Restoration

verfasst von : Yunsen Wang, Yong Wang, Daqiang Feng, Jing Yang, Ke An

Erschienen in: Proceedings of the 7th China High Resolution Earth Observation Conference (CHREOC 2020)

Verlag: Springer Nature Singapore

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Abstract

Neural network is widely used in remote sensing image restoration. To avoid huge time and space consumption of conventional neural network models, a novel remote sensing image restoration algorithm base on improved RBF (Radial Basis Function) neural network is proposed. The training data set could be organized by degrading the high-quality remote sensing images or promoting degraded images’ quality by other necessary methods. And the number of hidden layer is decided by the size of training data at the condition of small training sample scale. To accelerate convergence speed of RBF neural network during training process, conjugate gradient descent method is adopted to realize the weight parameters’ iterative correction. To further reduce calculating time, this paper proposes a matrix factorization algorithm to realize matrix's parallel arithmetic. Simulations and experiments indicate that the improved RBF neural network model could acquire relatively approving remote sensing image restoration results and time overhead.

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Vorheriges Kapitel Top-of-Atmosphere Radiance Image Simulation from the Visible to Thermal Infrared
Nächstes Kapitel Distributed HRWS MIMO-SAR Linear Vertical Baseline Estimation and Compensation
Literatur
1.
Jia T, Shi Y, Zhu Y, Wang L (2016) An image restoration model combining mixed L1/L2 fidelity terms. J Vis Commun Image R 38:461–473CrossRef
2.
Cai X, Chan R, Zeng T (2013) A two-stage images segmentation method using a convex variant of the Mumford-Shah model and thresholding. SIAM J Imag Sci 6:368–390MathSciNetCrossRef
3.
He L, Cui G, Feng H, Xu Z, Li Q, Chen Y (2015) The optimal code searching method with an improved criterion of coded exposure for remote sensing image restoration. Optics Commun 338:540–550CrossRef
4.
Ye Z (2017) Optical remote sensing motion degration simulation and image restoration technology, Zhejiang University
5.
Wu X, Wu J, Zhang H (2011) Research on image restoration techniques based on inverse filtering algorithm, Information. Technology 35:183–185
6.
Richardson WH (1972) Bayesian-based iterative method of image restoration. J Opt Soc Am 62:55–59CrossRef
7.
Lucy L (1974) An iterative technique for the rectification of observed distributions. Astron J 79:745–753CrossRef
8.
Khan M, Nizami IF, Majid M (2019) No-reference image quality assessment using gradient magnitude and wiener filtered wavelet features. Multimedia Tools Appl 78:85–14509
9.
Levin A, Fergus R, Durand F et al (2007) Image and depth from a conventional camera with a coded aperture. ACM Trans Graphic 26:70
10.
Krishnan D, Fergus R (2009) Fast image deconvolution using hyper-Laplacian priors. In: Proceedings of neural information processing systems (NIPS), pp 1033–1041
11.
Wang Y, Mu W, Du X, Ma C, Shen X (2020) Remote sensing image on-board restoration based on adaptive wiener filter. Lecture Notes in Electrical Engineering 657:271–282CrossRef
12.
Liu L, Li S, Lai S (2019) Advance of neural network in degraded image restoration. J Graph 40:213–224
13.
Sun J, Cao W, Xu Z et al (2015) Learning a convolutional neural network for non-uniform motion blur removal. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. IEEE Press, New York, pp 769–777
14.
Nah S, Kim TH, Lee KM (2017) Deep multi-scale convolutional neural network for dynamic scene deblurring. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE Press, New York, p 3
15.
Zhou YT, Chellappa R (1988) Image restoration using a network. IEEE Trans Acoust Speech Signal Process 36(1988):1141–1151
16.
Han Y, Wu L (2002) Image restoration using a modified Hopfield neural network of continuous state change. Signal Process 20:431–435
17.
Perry SW, Guan L (2000) Weight assignment for adaptive image restoration by neural networks. IEEE Trans Neural Netw 11:156–170CrossRef
18.
Kupyn O, Budzan V, Mykhailych M et al (2018) DeblurGAN: blind motion deblurring using conditional adversarial networks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 8183–8192
19.
Tang J, Wang K, Zhang W et al (2020) A deep learning-based image restoration method in optical synthetic aperture imaging system. Acta Optica Sinica 40:2111001
20.
Li Y, Xu Q, Li K (2020) New method of residual dense generative adversarial networks for image restoration. J Chin Comput Syst 41:830–836
Metadaten
Titel
Application of Improved RBF Neural Network in Remote Sensing Image Restoration
verfasst von
Yunsen Wang
Yong Wang
Daqiang Feng
Jing Yang
Ke An
Copyright-Jahr
2022
Verlag
Springer Nature Singapore
Buch
Proceedings of the 7th China High Resolution Earth Observation Conference (CHREOC 2020)

Print ISBN: 978-981-16-5734-4

Electronic ISBN: 978-981-16-5735-1

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-981-16-5735-1_32

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