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A study on color vegetation canopy images denoising algorithm

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Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Due to the characteristics of color vegetation canopy images which have multiple details and Gaussion noise interference, the adaptive mean filtering (AMF) algorithm is used to perform the denoising experiments on noised images in RGB and YUV color space. Based on the single color characteristics of color vegetation canopy images, a simplified AMF algorithm is proposed in this paper to shorten the overall running time of the denoising algorithm by simplifying the adaptive denoising processing of the component V, which contains less image details. Experimental results show that this method can effectively reduce the running time of the algorithm while maintaining a good denoising effect.
Rocznik
Strony
609--626
Opis fizyczny
Bibliogr. 30 poz., rys., tab.
Twórcy
autor
  • College of Information and Computer Engineering, Northeast Forestry University, Harbin 150001, China
autor
  • College of Information and Computer Engineering, Northeast Forestry University, Harbin 150001, China
autor
  • College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150001, China
Bibliografia
  • [1] Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, and G. Wang, “Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss”, IEEE Trans. Med. Imaging 37(6), 1348‒1357 (2018).
  • [2] K. Zhang, W. Zuo, and L. Zhang, “FFDNet: Toward a fast and flexible solution for CNN-based image denoising”, IEEE Trans. Image Process 27(9), 4608‒4622 (2018).
  • [3] S. Mukhopadhyay, S. Pratiher, S. Mukherjee, D. Dasgupta, N. Ghosh, and P. K. Panigrahi, “A two-stage framework for DIC image denoising and Gabor based GLCM feature extraction for pre-cancer diagnosis”, High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management 10505, 1050512 (2018).
  • [4] A. Kumar, M.O. Ahmad, and M.N.S. Swamy, “Tchebichef and Adaptive Steerable Based Total Variation Model for Image Denoising”, IEEE Trans. Image Process 28(6), 2921‒2035 (2019).
  • [5] B. Goyal, A. Dogra, S. Agrawal, and B.S. Sohi, “Two-dimensional gray scale image denoising via morphological operations in NSST domain & bitonic filtering”, Future Gener. Comput. Syst. 82, 158‒175 (2018).
  • [6] H.M. Ali, “MRI medical image denoising by fundamental fil ters”, High-Resolution Neuroimaging-Basic Physical Principles and Clinical Applications, Intech, 11‒124 (2018).
  • [7] H. Dawood, M. Iqbal, M. Azhar, H. Ahmad, H. Dawood, Z. Mehmood, and J.S. Alowibdi, “Texture-preserving denoising method for the removal of random-valued impulse noise in gray-scale images”, Opt. Eng. 58(2), 023103 (2019).
  • [8] W. Zhu, E. Wang, Y. Hou, L. Xian, and M.A. Ashraf, “Hybrid Filtering Optimization Method for Denoising Contaminated Spot Images at Near-Sea-Surface Intervals”, Journal of Coastal Research 82, 70‒76 (2018).
  • [9] N. Muhammad, N. Bibi, A. Jahangir, and Z. Mahmood, “Image denoising with norm weighted fusion estimators”, Pattern Analysis and Applications 21(4), 1013‒1022 (2018).
  • [10] B. Du, Z. Huang, N. Wang, Y. Zhang, and X. Jia, „Joint weighted nuclear norm and total variation regularization for hyperspectral image denoising”, Int. J. Remote Sens. 39(2), 334‒355 (2018).
  • [11] M.A. Soto, J.A. Ramírez, and L. Thévenaz, “Optimizing Image Denoising for Long-Range Brillouin Distributed Fiber Sensing”, J. Lightwave Technol. 36(4), 1168‒1177 (2018)
  • [12] A. Stankiewicz, T. Marciniak, A. Dąbrowski, M. Stopa, P. Rakowicz, and E. Marciniak, “Denoising methods for improving automatic segmentation in OCT images of human eye”, Bul. Pol. Ac.: Tech. 65(1), 71‒78 (2017).
  • [13] H. Wang, Y. Cen, Z. He, R. Zhao, and F. Zhang, “Reweighted low-rank matrix analysis with structural smoothness for image denoising”, IEEE Trans. Image Process. 27(4), 1777‒1892 (2018).
  • [14] S. Wu, T. Fan, C. Dong, and Y. Qiao, “RDS-Denoiser: a Detail-preserving Convolutional Neural Network for Image Denoising”, 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), 127‒132 (2018).
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  • [16] Y.R. Fan, T.Z. Huang, X.L. Zhao, L.J. Deng, and S. Fan, “Multi-spectral Image Denoising via Nonlocal Multitask Sparse Learning”, Remote Sens. 10(1), 116 (2018).
  • [17] X. Zheng, Y. Yuan, and X. Lu, “Hyperspectral Image Denoising by Fusing the Selected Related Bands”, IEEE Trans. Geosci. Remote Sens. 57(5), 2596‒2609 (2018).
  • [18] H.R. Shahdoosti and S.M. Hazavei, “Combined ripplet and total variation image denoising methods using twin support vector machines”, Multimedia Tools and Applications 77(6), 7013‒7031 (2018).
  • [19] L. Fan, X. Li, Q. Guo, and C. Zhang, “Nonlocal image denoising using edge-based similarity metric and adaptive parameter selection” Science China Information Sciences 61(4), 049101 (2018).
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Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-be09de0e-37a1-4f41-a742-dea018c118e2
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