Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods.

中文翻译：

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快速准确的自动笔触提取

笔触被视为艺术家在绘画中的“笔迹”。在风格学习和迁移、模仿绘画和绘画认证等许多应用中，非常需要使用计算机程序从古代大师的作品中定量准确地识别笔触特征。然而，由于绘画中成百上千笔交织的性质，它仍然具有挑战性。本文提出了一种基于深度神经网络的高效画笔笔画提取算法，即DStroke。与最先进的研究相比，所提出的 DStroke 的主要优点是无需手动注释即可自动快速地从绘画中提取笔触，同时以高可靠性准确地近似真实的笔触。在此处，恢复笔触之间忠实的柔和过渡通常被其他方法忽略。事实上，一幅杰作中的笔触细节（例如，形状、颜色、纹理、重叠）是艺术家们非常需要的，因为它们有望增强和扩展艺术家的能力，就像显微镜扩展生物学家的能力一样。为了证明所提出的 DStroke 的高效率，我们分别在一组真实的绘画扫描和一组合成绘画上执行它。实验表明，提出的 DStroke 在识别和提取笔触方面明显更快、更准确，优于其他方法。重叠）是艺术家们非常需要的，因为它们有望增强和扩展艺术家的能力，就像显微镜扩展生物学家的能力一样。为了证明所提出的 DStroke 的高效率，我们分别在一组真实的绘画扫描和一组合成绘画上执行它。实验表明，提出的 DStroke 在识别和提取笔触方面明显更快、更准确，优于其他方法。重叠）是艺术家们非常需要的，因为它们有望增强和扩展艺术家的能力，就像显微镜扩展生物学家的能力一样。为了证明所提出的 DStroke 的高效率，我们分别在一组真实的绘画扫描和一组合成绘画上执行它。实验表明，提出的 DStroke 在识别和提取笔触方面明显更快、更准确，优于其他方法。