Robust adaptive directional lifting wavelet transform for image denoising

Recent researches have shown that the adaptive directional lifting (ADL) can represent edges and textures in images effectively. This makes it possible to separate noise from image signal distinctly in image denoising. However, a key issue named orientation estimation for ADL becomes inefficient and error prone in the noised circumstance. The authors propose a robust adaptive directional lifting-based (RADL) wavelet transform for image denoising by constructing ADL in an anti-noise way. In our method, a simple model of pixel pattern classification is incorporated into orientation estimation module to strengthen the robustness of this algorithm. Moreover, instead of determining the transform strategy based on sub-blocks, RADL is performed on pixel-level to pursue better denoising results. Experimental results show that the proposed technique demonstrates both PSNR and visual quality improvement on images with rich textures.