Simplicity Driven Edge Refinement and Color Reconstruction in Image Vectorization

Abstract

Gestalt psychology indicates that simplicity is central to image vectorization, i.e., observers tend to perceive jagged raster edges as piecewise smooth curves and color changes as being either gradual (along edges) or abrupt (across edges). In this paper, we give a pair of simplicity-driven formulations to respectively cope with the two challenges. In detail, we formulate the underlying as-rigid-as-possible edges as the axes of symmetry of the edge saliency map, while reconstructing the color field by enforcing the fidelity and the smoothness at the same time (except on the detected boundaries). We finally convert a rasterized image into gradient-aware vector graphics whose base domain is a high-quality triangle mesh. On the one hand, the rigidity of the boundary curves is naturally achieved based on the assumption of simplicity, instead of by an empirically-grounded curve fitting operation; on the other hand, the color of near-boundary regions is inferred by Hessian energy (an extrapolation-like technique). Our vectorization method is able to yield more visually realistic results than existing approaches and is useful in flexible recoloring, shape editing, and hierarchical level-of-detail (HLOD) image representation.