Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Complementary tensor-driven image coherence diffusion for oriented structure enhancement | Hong-mei et al. EURASIP Journal on Advances in Signal Processing 2011 2011 70 http content 2011 1 70 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Complementary tensor-driven image coherence diffusion for oriented structure enhancement Zhang Hong-mei Wan Ming-xi and Bian Zheng-zhong Abstract Oriented structure enhancement plays important role in computer vision tasks where the diffusion is encouraged along the preferred direction instead of perpendicular to it. By analyzing the differential geometric property of the oriented structure a complementary tensor is proposed by combining the first and the second-order structure tensors as complementary descriptors which can precisely analyze not only the step edges but also the weak edges such as narrow peak or ridge-like structures. Complementary diffusion tensor is constructed from the new structure tensor which steers coherence diffusion for oriented structure enhancement. Furthermore fast algorithm based on additive operator splitting scheme is used for numerical solution which is much faster than usual approach. The experimental results on several images are provided. Experiments show that the image diffusion process steered by the new complementary tensor can strengthen the oriented structures and also close the interrupted lines as well. Both strong and weak edges are enhanced while noise is removed. Our approach is very promising and could be applied to many other images. Keywords tensor driven image diffusion coherence-enhancing structure tensor diffusion tensor second-order directional derivative AOS scheme 1. Introduction Image enhancement is an important preprocessing step that removes noise while preserving semantically important structures such as edges and oriented structures. This may give great help for simplifying subsequent image analysis like segmentation and understanding. In recent years nonlinear PDE-based diffusion for image
