A hybrid edge-preserving image smoothing scheme for noise removal

In this paper, we propose a new image denoising scheme that is an integration of a content-adaptive guided filter and a collaborative Wiener filter. The proposed scheme consists of two steps. First a content-adaptive guided filter, which smoothes image based on spatial similarity within a local window, is applied. The content-adaptive guided filter can efficiently preserve edges while smoothing noise. A preliminary estimation of noise-free image can be obtained by the content-adaptive guided filter. In the second step, a patch-grouping based collaborative Wiener filter is adopted to exploit non-local similarity, and outputs final denoised image. Compared to the state-of-the-art denoising scheme, BM3D, the proposed method is more efficient in computation. Moreover, simulation results have shown that the proposed method can achieve comparable PSNR values and better visual quality on denoising of textural images.