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- W2739123375 abstract "Common fuzzy radial basis function neural network (F-RBFNN) behaves the capability of the approximation of nonlinear noise signal and can denoise images well. In F-RBFNN, the knowledge expression of fuzzy logic and the reasoning ability are combined with the RBFNN’s capabilities of fast learning and generalization. And the F-RBFNN’s structure and parameters can be adjusted according to the real problem. But this model can not overcome the detect of redundant fuzzy rule so that the optimized learning speed is too slow. To avoid this problem, considering the modified structure and learning algorithm of the antecedent and subsequent network of F-RBFNN, an improved F-RBFNN is proposed and used to denoise millimeter wave (MMW) images. At the same time, to obtain high-quality restored image, the method of sparse representation with the self-adaptive denoising property is used again to denoise results obtained by F-RBFNN. Using the relative single noise ratio (RSNR) criterion to measure denoised images, simulation experimental results show that, compared with other denoising methods such as F-RBFNN, RBFNN and K-SVD and so on, this combination denoising method can obtain better restored images." @default.
- W2739123375 created "2017-07-31" @default.
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- W2739123375 date "2017-01-01" @default.
- W2739123375 modified "2023-09-26" @default.
- W2739123375 title "MMW Image Restoration Using the Combination Method of Modified Fuzzy RBFNN and Sparse Representation" @default.
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- W2739123375 doi "https://doi.org/10.1007/978-3-319-63309-1_29" @default.
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