Objetive: Nonlinearity of camera response function makes for complexity of CCD noise model and makes the traditional filter inefficient

so a wavelet neural network filter is proposed to remove CCD noise in digital images. Method: First

by analyzing the CCD noise model we find out the reason for the CCD noise model’s complexity---nonlinearity of camera response function and image intensity. Second

on the basis of analyzing ANS filter

two factors affecting the filter---filter window and image intensity are found. wavelet neural network (WNN) is used to approach the photon transfer curve (PTC)

classify the image according to the noise parameter and assign the coefficient

and then suited nonlinear filters are used to remove noise

at last all filters’ output are combined for the final output. Result: Experimental results indicate that WNN-NANS filter has a better filtering effect and increases the SNR（24.65）. Conclusion: Because of good approach to nonlinearity

neural network is combined with nonlinear ANS (Adaptive noise smoothing) filter for a new filter-WNN-NANS (neural network-nonlinear adaptive noise smoothing) filter. Experiment results show that the WNN filter is more efficient in noise removal

edge reservation and SNR.