Image restoration techniques based on fuzzy neural networks
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By establishing some suitable partitions of input and output spaces, a novel fuzzy neural network (FNN) which is called selection type FNN is developed. Such a system is a multilayer feedforward neural network, which can be a universal approximator with maximum norm. Based on a family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN is constructed. As a result, the fusion of selection type FNN and inference type FNN results in a novel filter—FNN filter. It is simple in structure. And also it is convenient to design the learning algorithm for structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed on image and preserve fine image structure, simultaneously. Some examples are simulated to confirm the advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzy mean (AWFM) filter and so on, in suppression of noises and preservation of image structure.
Keywordsfuzzy neural network selection type FNN inference type FNN FNN filter
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- 1.Castleman, K. R., Digital Image Processing, Beijing: Tsinghua University Press, Prentice Hall, Inc., 1998.Google Scholar
- 15.Liu Puyin, Representation of digital image by fuzzy neural networks, Fuzzy Sets and Systems, 2002(in press).Google Scholar
- 18.Liu Puyin, Li Hongxing, Approximation of generalized fuzzy system to integrable function, Science in China, Series E, 2000, 43(5): 618–628.Google Scholar