Image Vectorization with Depth: convexified shape layers with depth ordering

TL;DR

This paper introduces a novel image vectorization method that incorporates depth ordering and curvature-based inpainting to produce scalable, convex shape layers, improving boundary representation and editing capabilities.

Contribution

The paper proposes a new vectorization approach that integrates depth ordering, convexification, and curvature-based inpainting, enabling more natural shape decomposition and editing.

Findings

Effective removal of pixelization effects.

Improved shape boundary accuracy with curvature-based inpainting.

Enhanced image editing through layered shape decomposition.

Abstract

Image vectorization is a process to convert a raster image into a scalable vector graphic format. Objective is to effectively remove the pixelization effect while representing boundaries of image by scaleable parameterized curves. We propose new image vectorization with depth which considers depth ordering among shapes and use curvature-based inpainting for convexifying shapes in vectorization process.From a given color quantized raster image, we first define each connected component of the same color as a shape layer, and construct depth ordering among them using a newly proposed depth ordering energy. Global depth ordering among all shapes is described by a directed graph, and we propose an energy to remove cycle within the graph. After constructing depth ordering of shapes, we convexify occluded regions by Euler's elastica curvature-based variational inpainting, and leverage on the stability of Modica-Mortola double-well potential energy to inpaint large regions. This is following human vision perception that boundaries of shapes extend smoothly, and we assume shapes are likely to be convex. Finally, we fit B\'{e}zier curves to the boundaries and save vectorization as a SVG file which allows superposition of curvature-based inpainted shapes following the depth ordering. This is a new way to vectorize images, by decomposing an image into scalable shape layers with computed depth ordering. This approach makes editing shapes and images more natural and intuitive. We also consider grouping shape layers for semantic vectorization. We present various numerical results and comparisons against recent layer-based vectorization methods to validate the proposed model.