Among the variety of approaches proposed in literature, we can clearly distinguish the Wiener filter and the wavelet transform based ones for their effectiveness and, in many cases, simplicity. By exploiting the characteristics of both wavelet thresholding denoising and spatial Wiener filtering, the paper presents a combined scheme for the noise removal in images. We first perform thresholding denoising in wavelet domain to obtain a pre-denoised image, then spatial adaptive Wiener filter, i.e. Lee filtering, is used to increase the quality of the image restored. The crux of our method lies in the simple yet effective estimation of the optimal noise variance for Lee filter. By numerical computation, this optimal noise variance of Lee filter is presented which can nearly minimize the mean square error (MSE) of the pre-denoised image. Experiment results show that mean square error and signal-to-noise ratio (SNR) of our combined denoising approach have been improved, compared with the denoising solely in wavelet or spatial domain.