Edge‐preserving image denoising
Fenghua Guo, Caiming Zhang, Mingli Zhang · IET Image Processing · 2018
In image denoising, high‐frequency components are more notable to the human eyes than low‐frequency components. While high‐frequency components contain more variations and represent the detailed textures, the reconstructions of these components are much harder and it is a remaining challenge in image denoising. In this study, a novel edge‐preserving image denoising algorithm is proposed, it treats the low‐ and high‐frequency components of the image separately. For restoration of high‐frequency components, a neighbourhood regression method is proposed. An energy minimisation function is developed to combine the low‐ and high‐frequency components into one model. Experiments show that the proposed method outperforms the state‐of‐the‐art methods in peak signal‐to‐noise ratio, edges preservation and visual performance.