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Image Vectorization via Gradient Reconstruction
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AI
DOI：10.1111/cgf.70055.png)
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Abstract
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We present a fully automated technique that segments raster images into smooth shaded regions and reconstructs them using an optimal mix of solid fills, linear gradients, and radial gradients. Our method leverages a novel discontinuity-aware segmentation strategy and gradient reconstruction algorithm to accurately capture intricate shading details and produce compact B & eacute;zier curve representations. Extensive evaluations on both designer-created art and generative images demonstrate that our approach achieves high visual fidelity with minimal geometric complexity and fast processing times. This work offers a robust and versatile solution for converting detailed raster images into scalable vector graphics, addressing the evolving needs of modern design workflows.
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