Study of Image Local Scale Structure Using Nonlinear Diffusion

STUDY OF IMAGE LOCAL SCALE STRUCTURE USING NONLINEAR DIFFUSION Yan Wang James C. Gee Multi-scale representation and local scale extraction of images are important in computer vision research, as in general, structures within images are unknown. Traditionally, the multi-scale analysis is based on the linear diffusion (i.e. heat diffusion) with known limitation in edge distortions. In addition, the term scale which is used widely in multi-scale and local scale analysis does not have a consistent definition and it can pose potential difficulties in real image analysis, especially for the proper interpretation of scale as a geometric measure. In this study, in order to overcome limitations of linear diffusion, we focus on the multi-scale analysis based on total variation minimization model. This model has been used in image denoising with the power that it can preserve edge structures. Based on the total variation model, we construct the multi-scale space and propose a definition for image local scale. The new definition of local scale incorporates both pixel-wise and orientation information. This definition can be interpreted with a clear geometrical meaning and applied in general image analysis. The potential applications of total variation model in retinal fundus image analysis is explored. The existence of blood vessel and drusen struc-