A decision-tree-based denoising approach for efficient removal of impulse noise
Chien-Chuan Huang, Chih‐Yuan Lien, Pei‐Yin Chen · 2010
Images are often corrupted by impulse noise in the procedures of image acquisition and transmission. In this paper, we propose a novel denoising method, which is based on the decision-tree and edge-preserving techniques, for the removal of random-valued impulse noise. Extensive experimental results show that the proposed technique not only preserves the edge features, but also obtains excellent performances in terms of quantitative evaluation and visual quality. Furthermore, the design requires only low computational complexity. It is very suitable for real-time embedded systems.