Image denoising based on improved adaptive directional lifting wavelet transform

In this paper we propose a new denoising technique based on improved adaptive directional lifting wavelet transform (ADL). Using this method can separate noise from image signal distinctly by the extraordinary ability of ADL to represent the edges and textures. However, in the smooth regions of a noisy image, ADL is expensive and inaccurate. Therefore, we construct the ADL in an anti-noise way based on pixel pattern classification. Compared with the traditional ADL for image compression, there is no restriction of side-information, so the optimal strategies of direction determination and transformation can be selected by the judgment of different pixel patterns. Experimental results show that the proposed technique outperforms traditional wavelet and lifting scheme in both PSNR and visual quality, especially for the images with rich texture features such as remote sensing images.