PT Journal
AU Debora Gil
   Petia Radeva
TI A Regularized Curvature Flow Designed for a Selective Shape Restoration
SO IEEE Transactions on Image Processing
PY 2004
BP 1444–1458
VL 13
DI 10.1109/TIP.2004.836181
DE Geometric flows; nonlinear filtering; shape recovery.
AB Among all filtering techniques, those based exclu- sively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves non-trivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization.
ER