Open Access
Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
---|---|---|
Article Number | 04084 | |
Number of page(s) | 5 | |
Section | Circuit Simulation, Electric Modules and Displacement Sensor | |
DOI | https://doi.org/10.1051/matecconf/201823204084 | |
Published online | 19 November 2018 |
- Wu Qiong, Application of Wavelet on Image Edge Detection and Denoising [D], Tianjin University(2008). [Google Scholar]
- Donoho D L, Johnstone I M, Ideal spatial adaptation via wavelet shrinkage[J][C]// Biometrika(1994). [Google Scholar]
- Wang Yi, The Application of Wavelet Transform in Image Processing [D], Xidian University(2015). [Google Scholar]
- He Cunfu, Liu Shuo, et al., Application of Wavelet Denoise in Defect Inspection of Steel Strands[J]. Chinese Journal of Mechanical Engineering, (07):118-122( 2008). [Google Scholar]
- Li Xuchao, Zhu Shan-an. Survey of Wavelet Domain Image Denoising[J].Journal of Image and Graphics, (09):1201-1209(2006). [Google Scholar]
- Zhu Lei. Study on Algorithm of Image Denoising Based on Multiwavelet Transform[D]. Harbin Engineering University(2006). [Google Scholar]
- Gonzales, R.C. et al., Digital Image Processing[M], Electronic Industry Press(2010). [Google Scholar]
- Tang Bo, Kong Jianyi, et al., Wavelet threshold noise reduction of band steel surface defect image[J], Journal of Wuhan University of Science and Technology, 33(01):38-42,(2010). [Google Scholar]
- Wang Rui,Zhang Youchun, New threshold function in wavelet threshold de-noising[J], Computer Engineering and Applications, 49(15):215-218,(2013). [Google Scholar]
- Yuan Hongmei, Algorithm and Realization on Image Denoising Based on Wavelet Transform[D], Shanghai Jiaotong University(2008). [Google Scholar]
- Yan Bing, Wang Jinhe, Zhao Jing, Research of Image De-noising Technology Based on Mean Filtering and Wavelet Transformation[J], Computer Technology and Development,21(02):51-53+57,(2011). [Google Scholar]
- Wang Bei, Zhang Genyao, et al., Wavelet threshold denoising algorithm based on new threshold function[J], Journal of Computer Applications, 34(05):1499-1502,(2014). [Google Scholar]
- Wu Guangwen, Wang Changming, et al., A Wavelet Threshold De-noising Algorithm Based on Adaptive Threshold Function [J], Journal of Electronics and Information Technology, 36(06): 1340-1347,(2014). [Google Scholar]
- Zha Yvfei, Bi Duyan, Adaptive Wavelet Multi-thresholding for Image Denoising[J], Journal of Image and Graphics, (05): 567-570,(2005). [Google Scholar]
- Donoho D L, De-noising by soft-thresholding[J], IEEE Transactions on Information Theory, 41(3):613-627,(2002). [Google Scholar]
- Yang Rongchang, Study on Adaptive Image Denoising Algorithm Based on Image Quality Assessment[D], Nanchang University(2016). [Google Scholar]
- Yoon B J, Vaidyanathan P P, Wavelet-based denoising by customized thresholding[C]// IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings. IEEE, ii-925-8 vol.2,(2004). [Google Scholar]
- Chen Xiaoxi, Wang Yanjie, Liu Lian, Deep study on wavelet threshold method for image noise removing[J], LASER & INFRARED, 42(01):105-110,(2012). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.