Fuzzy diffusion filter with extended neighborhood

被引:12
作者
Elmas, Cetin [1 ]
Demirci, Recep [2 ]
Guvenc, Ugur [3 ]
机构
[1] Gazi Univ, Fac Technol, Dept Elect & Elect Engn, Ankara, Turkey
[2] Gazi Univ, Fac Technol, Dept Comp Engn, Ankara, Turkey
[3] Duzce Univ, Fac Technol, Dept Elect & Elect Engn, Duzce, Turkey
关键词
Extended neighborhood; Fuzzy similarity; Diffusivity; Image filter; ANISOTROPIC DIFFUSION; EDGE-DETECTION; SIMILARITY; SEGMENTATION; ALGORITHM; IMAGES; SPACE;
D O I
10.1016/j.eswa.2012.05.042
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Anisotropic diffusion filters, which are motivated from heat diffusion between mediums, have become a widely used technique in the field of image processing. In the initial proposals of anisotropic diffusion filters, 4-neighborhood values with diffusivity functions are computed independently for each spatial location because of numerical approximation. However, anisotropic diffusion filters could not be used in real-time image and video processing applications because they need diffusivity parameters, which must be specified by users in every sampling period. In this study, a fuzzy adaptive diffusion filter using extended neighborhood without diffusivity functions has been developed. The fuzzy adaptive diffusion filter does not require any parameter chosen by user and therefore they could be employed in real-time applications. In the fuzzy adaptive diffusion filter, a similarity transformation by means of relation matrix and fuzzy logic is carried out. Accordingly, the similarity image, output of transformation, is directly used as a heat diffusion coefficient in the diffusion filter. Results show that the fuzzy adaptive diffusion filter is very efficient for removing noise in image while preserving edges. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:866 / 872
页数:7
相关论文
共 28 条
[11]  
Elmas C., 2011, P 2 INT FUZZ SYST S, P69
[12]   A segmentation algorithm for SAR images based on the anisotropic heat diffusion equation [J].
Gao, Gui ;
Zhao, Lingjun ;
Zhang, Jun ;
Zhou, Diefei ;
Huang, Jijun .
PATTERN RECOGNITION, 2008, 41 (10) :3035-3043
[13]  
Guvenc U., 2008, THESIS GAZI U ANKARA
[14]   Similarity-based categorization and fuzziness of natural categories [J].
Hampton James, A .
COGNITION, 1998, 65 (2-3) :137-165
[15]   Recognizing spatial patterns: a noisy exemplar approach [J].
Kahana, MJ ;
Sekuler, R .
VISION RESEARCH, 2002, 42 (18) :2177-2192
[16]   An anisotropic diffusion based on diagonal edges [J].
Kim, Hye Suk ;
Yoo, Jae Myeong ;
Park, Mi Seon ;
Dinh, Toan Nguyen ;
Lee, Guee Sang .
9TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY: TOWARD NETWORK INNOVATION BEYOND EVOLUTION, VOLS 1-3, 2007, :384-+
[17]  
Li J, 2009, 2009 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS 1-7, CONFERENCE PROCEEDINGS, P1009
[18]  
Lu R, 2005, P ANN INT IEEE EMBS, P3402
[19]   Enhancement of the ultrasound images by modified anisotropic diffusion method [J].
Mittal, Deepti ;
Kumar, Vinod ;
Saxena, Suresh Chandra ;
Khandelwal, Niranjan ;
Kalra, Naveen .
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2010, 48 (12) :1281-1291
[20]   A new interpretation and improvement of the nonlinear anisotropic diffusion for image enhancement [J].
Monteil, J ;
Beghdadi, A .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1999, 21 (09) :940-946