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image vectorization using a sparse patch layout

GRAPHICAL MODELS 2025年 135卷

作者： He, K. Roerdink, J. B. T. M. Kosinka, J. Univ Groningen Bernoulli Inst Nijenborgh 9 NL-9747 AG Groningen Netherlands

Mesh-based image vectorization techniques have been studied for a long time, mostly owing to their compactness and flexibility in capturing image features. However, existing methods often lead to relatively dense meshes, especially when applied to images with high-frequency details or textures. We present a novel method that automatically vectorizes an image into a sparse collection of Coons patches whose size adapts to image features. To balance the number of patches and the accuracy of feature alignment, we generate the layout based on a harmonic cross field constrained by image features. We support T-junctions, which keeps the number of patches low and ensures local adaptation to feature density, naturally complemented by varying mesh-color resolution over the patches. Our experimental results demonstrate the utility, accuracy, and sparsity of our method.

关键词： Vector graphics image vectorization

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image vectorization with Depth: Convexified Shape Layers with Depth Ordering

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SIAM JOURNAL ON IMAGING SCIENCES 2025年第2期18卷 963-1001页

作者： Law, Ho Kang, Sung Ha Georgia Inst Technol Sch Math Atlanta GA 30332 USA

image vectorization is a process to convert a raster image into a scalable vector graphic format. The objective is to effectively remove pixelization effects while representing image boundaries by scalable parameterized curves. We propose a new image vectorization method which considers depth ordering among shapes and use curvature-based inpainting for convexifying shapes in the 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 cycles within the graph. After constructing a depth ordering of shapes, we convexify occluded regions by Euler's elastica curvature-based variational inpainting and leverage the stability of Modica-Mortola double-well potential energy to inpaint large regions. This is following human vision perception, where boundaries of shapes extend smoothly, and we assume that shapes are likely to be convex. Finally, we fit Be'\zier curves to the boundaries and store vectorization results as a scalable vector graphics file, allowing superposition of curvaturebased 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.

关键词： image vectorization segmentation with depth variational curvature-based inpainting

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A NOVEL ARCHITECTURE FOR image vectorization WITH INCREASING GRANULARITY 31

A NOVEL ARCHITECTURE FOR IMAGE VECTORIZATION WITH INCREASING...

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2024 International Conference on image Processing

作者： Huang, Junhao Zhang, Fang Liu, Meiliang Si, ZhengYe Zhao, Zhiwen Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China Engn Res Ctr Intelligent Technol & Educ Applicat Beijing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

In vector graphics, images are described by mathematical formulas with full image details even after scaling. Most research on generating vector graphics from raster images adopt the approach of splicing graphic fragments, which cannot perfectly retain the original topological structure and details of images. In this paper, we propose TSVec, a novel model for generating high-quality vector graphics by raster images, which takes advantages of the vision transformer and image super-resolution to enhance the granularity of vectorization, especially suitable for dealing with low-resolution raster images. The experimental results show that the vector graphics generated by TSVec outperform the current unsupervised vector generation models.

关键词： image vectorization Bezier Curve Fitting Attention Mechanism Deep Learning Super-Resolution

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EvoVec: Evolutionary image vectorization with Adaptive Curve Number and Color Gradients 18th

EvoVec: Evolutionary Image Vectorization with Adaptive Curve...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Bazhenov, Egor Jarsky, Ivan Efimova, Valeria Muravyov, Sergey ITMO Univ St Petersburg Russia

ISBN: (纸本)9783031700842;9783031700859

Vector and raster graphics are the two main types of 2D images used in computer graphics. Raster graphics are images consisting of pixels (dots);vector images are created using mathematical objects such as lines, curves, and shapes. The main advantage of vector graphics is that they can be scaled without loss of quality, which is useful for advertising, design, frontend development, and other fields of application. At the moment, the issue of vectorization (conversion from raster to vector graphics) has not been fully resolved. There are two main approaches: deterministic algorithms and machine learningbased algorithms. Both of these types are not able to work with a color gradient and have other disadvantages, such as artifacts for deterministic algorithms, and extremely long working time and predefined curve number for machine learning-based algorithms. To solve the problems of existing solutions, we propose an evolutionary algorithm for image vectorization. Its main idea is to iteratively improve vector images using mutations and crossover. The proposed algorithm does not require any necessary parameters other than the original image and can process color gradients. The results of comparison with existing solutions show that our algorithm qualitatively and quickly vectorize images. Particularly, our approach outperforms others in terms of pixel-by-pixel MSE by 15%. The implementation is publicly available (https://***/EgorBa/EvoVec-Evolutionary-image-vectorization).

关键词： image vectorization evolutionary algorithm machine learning vector graphics

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SAMVG: A MULTI-STAGE image vectorization MODEL WITH THE SEGMENT-ANYTHING MODEL 49

SAMVG: A MULTI-STAGE IMAGE VECTORIZATION MODEL WITH THE SEGM...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhu, Haokun Chong, Juang Ian Hu, Teng Yi, Ran Lai, Yu-Kun Rosin, Paul L. Shanghai Jiao Tong Univ Shanghai Peoples R China Cardiff Univ Cardiff Wales

ISBN: (纸本)9798350344868;9798350344851

Vector graphics are widely used in graphical designs and have received more and more attention. However, unlike raster images which can be easily obtained, acquiring high-quality vector graphics, typically through automatically converting from raster images, remains a significant challenge, especially for more complex images such as photos or artworks. In this paper, we propose SAMVG, a multi-stage model to vectorize raster images into SVG (Scalable Vector Graphics). Firstly, SAMVG uses general image segmentation provided by the Segment-Anything Model and uses a novel filtering method to identify the best dense segmentation map for the entire image. Secondly, SAMVG then identifies missing components and adds more detailed components to the SVG. Through a series of extensive experiments, we demonstrate that SAMVG can produce high quality SVGs in any domain while requiring less computation time and complexity compared to previous state-of-the-art methods.

关键词： image vectorization Vector Graphics image Segmentation Computer Graphics Segment-Anything Model

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Towards High-fidelity Artistic image vectorization via Texture-Encapsulated Shape Parameterization

Towards High-fidelity Artistic Image Vectorization via Textu...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Ye Ni, Bingbing Liu, Jinfan Huang, Xiaoyang Chen, Xuanhong Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China USC SJTU Inst Cultural & Creat Ind Shanghai Peoples R China

ISBN: (纸本)9798350353006

We develop a novel vectorized image representation scheme accommodating both shape/geometry and texture in a decoupled way, particularly tailored for reconstruction and editing tasks of artistic/design images such as Emojis and Cliparts. In the heart of this representation is a set of sparsely and unevenly located 2D control points. On one hand, these points constitute a collection of parametric/vectorized geometric primitives (e.g., curves and closed shapes) describing the shape characteristics of the target image. On the other hand, local texture codes, in terms of implicit neural network parameters, are spatially distributed into each control point, yielding local coordinate-to-RGB mappings within the anchored region of each control point. In the meantime, a zero-shot learning algorithm is developed to decompose an arbitrary raster image into the above representation, for the sake of high-fidelity image vectorization with convenient editing ability. Extensive experiments on a series of image vectorization and editing tasks well demonstrate the high accuracy offered by our proposed method, with a significantly higher image compression ratio over prior art.

关键词： Geometric Primitives image vectorization Texture-Encapsulated

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VIVA: A VARIATIONAL image vectorization ALGORITHM ON DUAL-PRIMAL GRAPH PAIRS 30

VIVA: A VARIATIONAL IMAGE VECTORIZATION ALGORITHM ON DUAL-PR...

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30th IEEE International Conference on image Processing (ICIP)

作者： He, Yuchen Kang, Sung Ha Morel, Jean-Michel Shanghai Jiao Tong Univ Inst Nat Sci Shanghai Peoples R China Georgia Inst Technol Sch Math Atlanta GA USA City Univ Hong Kong Dept Math Kowloon Tong Hong Kong Peoples R China

ISBN: (纸本)9781728198354

We propose a novel variational image vectorization algorithm (VIVA) which alternatively smooths contours by affine shortening flow and eliminates spurious regions by minimizing a Mumford-Shah-type functional. We introduce dual-primal graphs representing domain partitions which allows for effective iterative computation. The method provides varying levels of simplicity on the topology of the resulted vector graphics while effectively removing pixelization. It compares favorably to the state-of-the-art (SOTA) vectorization methods.

关键词： image vectorization affine-shortening flow Mumford-Shah functional optimization

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Simplicity Driven Edge Refinement and Color Reconstruction in image vectorization 38th

Simplicity Driven Edge Refinement and Color Reconstruction i...

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38th Computer Graphics International Conference (CGI)

作者： Zhang, Zheng Zhao, Junhao Xin, Shiqing Chen, Shuangmin Zhou, Yuanfeng Tu, Changhe Wang, Wenping Shandong Univ Qingdao Peoples R China Qingdao Univ Sci & Technol Qingdao Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783030890285;9783030890292

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.

关键词： image vectorization Hessian energy Centroidal voronoi diagram image reconstruction

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Depth-aware image vectorization and editing

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VISUAL COMPUTER 2019年第6-8期35卷 1027-1039页

作者： Lu, Shufang Jiang, Wei Ding, Xuefeng Kaplan, Craig S. Jin, Xiaogang Gao, Fei Chen, Jiazhou Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China Univ Victoria Visual Comp Grp Victoria BC Canada Zhejiang Univ State Key Lab CAD & CG Hangzhou Zhejiang Peoples R China Univ Waterloo Comp Sci Waterloo ON Canada

image vectorization is one of the primary means of creating vector graphics. The quality of a vectorized image depends crucially on extracting accurate features from input raster images. However, correct object edges can be difficult to detect when color gradients are weak. We present an image vectorization technique that operates on a color image augmented with a depth map and uses both color and depth edges to define vectorized paths. We output a vectorized result as a diffusion curve image. The information extracted from the depth map allows us more flexibility in the manipulation of the diffusion curves, in particular permitting high-level object segmentation. Our experimental results demonstrate that this method achieves high reconstruction quality and provides greater control in the organization and editing of vectorized images than existing work based on diffusion curves.

关键词： image vectorization RGB-D images Depth aware Diffusion curves Object segmentation and editing

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Single image Super-resolution using vectorization and Texture Synthesis 16

Single Image Super-resolution using Vectorization and Textur...

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16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 16th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Hui, Kaoning Lee, Dongeun Wang, Tianyang Texas A&M Univ Dept Comp Sci & Informat Syst Commerce TX 75428 USA Austin Peay State Univ Dept Comp Sci & Informat Technol Clarksville TN 37044 USA

ISBN: (纸本)9789897584886

image super-resolution is a very useful tool in science and art. In this paper, we propose a novel method for single image super-resolution that combines image vectorization and texture synthesis. image vectorization is the conversion from a raster image to a vector image. While image vectorization algorithms can trace the fine edges of images, they will sacrifice color and texture information. In contrast, texture synthesis techniques, which have been previously used in image super-resolution, can reasonably create high-resolution color and texture information, except that they sometimes fail to trace the edges of images correctly. In this work, we adopt the image vectorization to the edges of the original image, and the texture synthesis based on the Kolmogorov-Smirnov test (KS test) to the non-edge regions of the original image. The goal is to generate a plausible, visually pleasing detailed higher resolution version of the original image. In particular, our method works very well on the images of natural animals.

关键词： SISR image vectorization Texture Synthesis KS Test

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