Blind Image Restoration by Combining Wavelet Transform and RBF Neural Network
Blind Image Restoration by Combining Wavelet Transform and RBF Neural Network
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DOI：
10.1142/s0219691307001562
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Ping Guo;Hongzhai Li;Michael R. Lyu
中科院分区：
文献类型：
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作者：
Ping Guo;Hongzhai Li;Michael R. Lyu
在本文中，我们提出了一种新的技术，用于恢复模糊噪声图像的模糊函数和噪声的统计没有任何先验知识。该技术结合小波变换和径向基函数（RBF）神经网络恢复给定的图像，高斯模糊和加性噪声退化。该方法利用小波变换将降质图像分解为高频部分和低频部分。然后，基于RBF神经网络的技术被用来恢复从给定的图像的底层图像。采用逆主元法（IPEM）加快了计算速度。实验结果表明，该方法继承了小波变换和IPEM的优点，具有计算效率高、抗噪能力强等优点。
In this paper, we present a novel technique for restoring a blurred noisy image without any prior knowledge of the blurring function and the statistics of noise. The technique combines wavelet transform with radial basis function (RBF) neural network to restore the given image which is degraded by Gaussian blur and additive noise. In the proposed technique, the wavelet transform is adopted to decompose the degraded image into high frequency parts and low frequency part. Then the RBF neural network based technique is used to restore the underlying image from the given image. The inverse principal element method (IPEM) is applied to speed up the computation. Experimental results show that the proposed technique inherited the advantages of wavelet transform and IPEM, and the algorithm is efficient in computation and robust to the noise.