Ultrafast technique of impulsive noise removal with application to microarray image denoising

被引:0
|
作者
Smolka, B
Plataniotis, KN
机构
[1] Silesian Tech Univ, Dept Automat Control, PL-44100 Gliwice, Poland
[2] Univ Toronto, Edward S Rogers Sr Dept Elect & Comp Engn, Toronto, ON M5S 3G4, Canada
来源
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper a novel approach to the impulsive noise removal in color images is presented. The proposed technique employs the switching scheme based on the impulse detection mechanism using the so called peer group concept. Compared to the vector median filter, the proposed technique consistently yields better results in suppressing both the random-valued and fixed-valued impulsive noise. The main advantage of the proposed noise detection framework is its enormous computational speed, which enables efficient filtering of large images in real-time applications. The proposed filtering scheme has been successfully applied to the denoising of the cDNA microarray images. Experimental results proved that the new filter is capable of removing efficiently the impulses present in multichannel images, while preserving their textural features.
引用
收藏
页码:990 / 997
页数:8
相关论文
共 50 条
  • [41] Significant Image Enhancement Technique for Removal of Noise in LiDaR Images
    Vijaya, A.
    Sundaresan, M.
    PROCEEDINGS OF THE 10TH INDIACOM - 2016 3RD INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT, 2016, : 3904 - 3908
  • [42] A Holistic Approach to Cross-Channel Image Noise Modeling and its Application to Image Denoising
    Nam, Seonghyeon
    Hwang, Youngbae
    Matsushita, Yasuyuki
    Kim, Seon Joo
    2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 1683 - 1691
  • [43] Image noise reduction by denoising autoencoder
    Yasenko, Lev
    Klyatchenko, Yaroslav
    Tarasenko-Klyatchenko, Oksana
    2020 IEEE 11TH INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS, SERVICES AND TECHNOLOGIES (DESSERT): IOT, BIG DATA AND AI FOR A SAFE & SECURE WORLD AND INDUSTRY 4.0, 2020, : 351 - 355
  • [44] Noise analysis and image denoising for DEI
    Wernick, MN
    Brankov, JG
    Saiz-Herranz, A
    DEVELOPMENTS IN X-RAY TOMOGRAPHY IV, 2004, 5535 : 660 - 668
  • [45] Image denoising based on noise detection
    Jiang, Yuanxiang
    Yuan, Rui
    Sun, Yuqiu
    Tian, Jinwen
    2017 INTERNATIONAL SYMPOSIUM ON APPLICATION OF MATERIALS SCIENCE AND ENERGY MATERIALS (SAMSE 2017), 2018, 322
  • [46] Wavelet denoising of hybrid noise in image
    Nanjing Research Institute of Electronics Technology, Nanjing 210013, China
    Yi Qi Yi Biao Xue Bao, 2007, SUPPL. 5 (309-311):
  • [47] Noise to Noise Ensemble Learning for PET Image Denoising
    Chan, Chung
    Zhou, Jian
    Yang, Li
    Qi, Wenyuan
    Asma, Evren
    2019 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC), 2019,
  • [48] Improved detection in correlated impulsive noise using wavelet denoising
    Burley, S
    Darnell, M
    Prowse, C
    Chandler, R
    MILCOM 97 PROCEEDINGS, VOLS 1-3, 1997, : 54 - 58
  • [49] Color image deblurring with impulsive noise
    Bar, L
    Brook, A
    Sochen, N
    Kiryati, N
    VARIATIONAL, GEOMETRIC, AND LEVEL SET METHODS IN COMPUTER VISION, PROCEEDINGS, 2005, 3752 : 49 - 60
  • [50] Image Deblurring in the Presence of Impulsive Noise
    Leah Bar
    Nahum Kiryati
    Nir Sochen
    International Journal of Computer Vision, 2006, 70 : 279 - 298