Pores-Preserving Face Cleaning Based on Improved Empirical Mode Decomposition

【摘要】 <正>In this paper,we propose a novel method of cleaning up facial imperfections such as bumps and blemishes that may detract from a pleasing digital portrait.Contrasting with traditional methods which tend to blur facial details, our method fully retains fine scale skin textures(pores etc.) of the subject.Our key idea is to find a quantity,namely normalized local energy,to capture different characteristics of fine scale details and distractions,based on empirical mode decomposition,and then build a quantitative measurement of facial skin appearance which characterizes both imperfections and facial details in a unified framework.Finally,we use the quantitative measurement as a guide to enhance facial skin.We also introduce a few high-level,intuitive parameters for controlling the amount of enhancement.In addition,an adaptive local mean and neighborhood limited empirical mode decomposition algorithm is also developed to improve in two respects the performance of empirical mode decomposition.It can effectively avoid the gray spots effect commonly associated with traditional empirical mode decomposition when dealing with high-nonstationary images.更多还原

【Abstract】 In this paper,we propose a novel method of cleaning up facial imperfections such as bumps and blemishes that may detract from a pleasing digital portrait.Contrasting with traditional methods which tend to blur facial details, our method fully retains fine scale skin textures(pores etc.) of the subject.Our key idea is to find a quantity,namely normalized local energy,to capture different characteristics of fine scale details and distractions,based on empirical mode decomposition,and then build a quantitative measurement of facial skin appearance which characterizes both imperfections and facial details in a unified framework.Finally,we use the quantitative measurement as a guide to enhance facial skin.We also introduce a few high-level,intuitive parameters for controlling the amount of enhancement.In addition,an adaptive local mean and neighborhood limited empirical mode decomposition algorithm is also developed to improve in two respects the performance of empirical mode decomposition.It can effectively avoid the gray spots effect commonly associated with traditional empirical mode decomposition when dealing with high-nonstationary images.更多还原