Millimeter wave image restoration based on fuzzy radial basis function neural networks and sparse representation

Abstract

As to the problems that Millimeter Wave(MMW) image is contaminated by much unknown noise and has lower resolution,and considering the non-linear filter property of Fuzzy Radial Basis Function Neural Network(F-RBFNN) and the self-adaptive denoising property of Sparse Representation(SR) based on K-Singular Value Decomposition(K-SVD),a MMW restoration method was proposed by combining F-RBFNN and sparse representation.In F-RBFNN,the knowledge expression of fuzzy logic and the reasoning ability were combined with the RBFNN's capabilities of fast learning and generalization.In order to realize the non-linear filtering to the MMW image,F-RBFNN's structure and parameters were adjusted according to the real problem.Furthermore,utilizing the advantages of sparse representation method,which the sparse representation behaves the visual characteristic and can denoise effectively when maintaining features of the object,the training results of F-RBFNN were locally denoised once again,and the MMW image with high resolution was obtained.Using the Relative Single Noise Ratio(RSNR) criterion to measure the quality of denoised images,the simulation results show that,compared with other denoising methods such as F-RBFNN,K-SVD denoising,and wavelet denoising,the proposed method combining F-RBFNN and SR can better restore the quality of MMW image.