针对已有的图像抽象化算法难以反映显著性边缘信息的不足,提出了一个显著性边缘引导下的基于能量优化的图像抽象化算法。对于给定的输入图像,首先基于边缘信息传递策略来提取图像的显著性边缘图,从而有效地减少了不连续边缘的产生;然后,为了在增强显著性边缘的同时抑制杂乱细节信息,根据显著性边缘图来构建图像的期望梯度场;最后,在图像颜色信息和期望梯度场的约束之下,通过能量优化来获得图像的抽象化效果。实验结果表明,本文算法在显著性边缘的连续性保持上表现出明显的优势,具有很好的科学研究价值及实际应用前景。

Considering the shortage of salient edge information for existing image abstraction methods, we present a salient edge guided approach based on energy optimization. For an input image, we first construct a salient edge map by a proposed edge information passing scheme which can effectively reduce the discontinuity of long edges. Then, in order to accentuate the details of the salient edges while suppressing the tangle-some details, we build an expected image gradient field in terms of the salient edge map. Finally, constrained by the image color information and the expected gradient field, we get the rendering result by minimizing a devised energy function. The experiment results show that our method has obvious advantage in the preservation of the edge coherence.