Defocused Image Restoration Using RBF Network and Kalman Filter
Yu–Gang Jiang, Qing Wu, Ping Guo · 2006
A novel defocused image restoration technique is proposed, which is based on radial basis function (RBF) neural network and Kalman filter. In this technique, firstly a RBF neural network is trained in wavelet domain to estimate defocus parameter. After obtaining the point spread function (PSF) parameter, Kalman filter is adopted to complete the restoration. We experimentally illustrate its performance on simulated data and compare it with other methods. Results show that the proposed PSF parameter estimation technique is more robust to noise.