Histogram-based fuzzy colour filter for image restoration

被引:36
|
作者
Schulte, Stefan [1 ]
De Witte, Valerie [1 ]
Nachtegael, Mike [1 ]
Van der Weken, Dietrich [1 ]
Kerre, Etienne E. [1 ]
机构
[1] Univ Ghent, Dept Appl Math & Comp Sci, Fuzziness & Uncertainty Modelling Res Unit, B-9000 Ghent, Belgium
关键词
fuzzy filter; histogram; colour filter; impulse noise; image restoration;
D O I
10.1016/j.imavis.2006.10.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A new impulse noise reduction method for colour images, called histogram-based fuzzy colour filter (HFC), is presented in this paper. The HFC filter is particularly effective for reducing high-impulse noise in digital images while preserving edge sharpness. Colour images that are corrupted with noise are generally filtered by applying a greyscale algorithm on each colour component separately. This approach causes artefacts especially on edge or texture pixels. Vector-based filtering methods were successfully introduced to overcome this problem. In this paper, we discuss an alternative technique so that no artefacts are introduced. The main difference between the new proposed method and the classical vector-based methods is the usage of colour component differences for the detection of impulse noise and the preservation of the colour component differences. The construction of the HFC filter involves three steps: (1) the estimation of the original histogram of the colour component differences, (2) the construction of suitable fuzzy sets for representing the linguistic values of these differences and (3) the construction of fuzzy rules that determine the output. Extensive simulation results show that the proposed filter outperforms many well-known filters (including vector-based approaches). (C) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:1377 / 1390
页数:14
相关论文
共 50 条
  • [41] Study and Comparison on Histogram-Based Local Image Enhancement Methods
    Yao, Min
    Zhu, Changming
    2017 2ND INTERNATIONAL CONFERENCE ON IMAGE, VISION AND COMPUTING (ICIVC 2017), 2017, : 309 - 314
  • [42] Histogram-Based Unsupervised Domain Adaptation for Medical Image Classification
    Diao, Pengfei
    Pai, Akshay
    Igel, Christian
    Krag, Christian Hedeager
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VII, 2022, 13437 : 755 - 764
  • [43] Fast two-step histogram-based image segmentation
    Krstinic, D.
    Skelin, A. K.
    Slapnicar, I.
    IET IMAGE PROCESSING, 2011, 5 (01) : 63 - 72
  • [44] Wavelet Transform Coefficient Histogram-Based Image Enhancement Algorithms
    Xia, Junjun
    Panetta, Karen
    Agaian, Sos
    MOBILE MULTIMEDIA/IMAGE PROCESSING, SECURITY, AND APPLICATIONS 2010, 2010, 7708
  • [45] A NEW MULTI-SCALE FUZZY MODEL FOR HISTOGRAM-BASED DESCRIPTORS
    Chai, Lunshao
    Qin, Zhen
    Zhang, Honggang
    Guo, Jun
    Bhanu, Bir
    ELECTRONIC PROCEEDINGS OF THE 2013 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO WORKSHOPS (ICMEW), 2013,
  • [46] HAIRIS: A Method for Automatic Image Registration Through Histogram-Based Image Segmentation
    Goncalves, Hernani
    Goncalves, Jose Alberto
    Corte-Real, Luis
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2011, 20 (03) : 776 - 789
  • [47] Block histogram-based neural fuzzy approach to the segmentation of skin colors
    Department of Electrical Engineering, National Chung Hsing University, Taichung, 402, Taiwan
    J. Inf. Sci. Eng., 2007, 6 (1737-1752):
  • [48] Block histogram-based neural fuzzy approach to the segmentation of skin colors
    Juang, Chia-Feng
    Perng, Hwai-Sheng
    Chiu, Shih-Hsuan
    JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 2007, 23 (06) : 1737 - 1752
  • [49] Partition Fuzzy Median Filter for Image Restoration
    Rezaee, Alireza
    FUZZY INFORMATION AND ENGINEERING, 2021, 13 (02) : 199 - 210
  • [50] Fuzzy filter based on neural network and its application to image restoration
    Li, ST
    Wang, YN
    Zhang, CF
    Mao, JX
    2000 5TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS I-III, 2000, : 1133 - 1138