An improved kernel wiener filter for image denoising is proposed.Image denoising is done by wiener filter using linear transformation,which is minimizing the mean square error between the denoised image and original noiseless image.And the wiener filter can give the optimal solution in the means of Bayesian.But the linear transformation can′t meet the non-stationary process.It is a good method wiener filter of nonlinear transformation in feature space using kernel method.Usually,the nonlinear wiener filter in feature space encounters the problem of localminimum and sensitive to initial values,etc.There is internal relatedness between mean square error in feature space and input space in the Guassian kernel.So here the result of wiener filter in input space is constrained to the mean square error of wiener filter in feature space.The speed of convergence and optimal is improved.The signal information is recovered better.The results of denoising with improved kernel wiener filter are superior to traditional wiener filter and kernel wiener filter.