TIFVec: an Image Vectorization Approach Based on Texture Intensity Field

Image vectorization works to convert raster images into vector graphics, which is widely used in various fields. The current state-of-the-art approaches are learning-based models, which aims to establish the correlation between the raster image and a specific number of randomly distributed primitives through deep learning, such as Bézier curves. However, these methods have not payed attention to the influence of some important factors on the performance of vectorization, such as the number of primitives and the initial primitive positions. Therefore, the converted vector outputs usually suffer from various shortcomings such as excessively high number of primitives, unclear rendering of details, color mean errors, and prolonged primitives optimization time. To address the aforementioned issues, we propose an image vectorization framework termed TIFVec, which takes the Bézier curves as primitives and discovers the interdependent mechanism among different factors. In the framework, we introduce the texture intensity field TIF , which is able to guide the optimization of those factors above, in terms of the primitive initialization strategy and TIF-based objective function. Based on TIF, the connections among different factors can be constructed, and the performance of vectorization can be effectively improved. The experimental results demonstrate that our method significantly outperforms the current state-of-the-art models across multiple datasets, in terms of the visual results, evaluation indicators, and primitives optimization time.