Wang Xiaojun, Jiang Jing
College of Arts, Anhui Xinhua University, Hefei, China.
PLoS One. 2025 Jan 14;20(1):e0313113. doi: 10.1371/journal.pone.0313113. eCollection 2025.
To improve the expressiveness and realism of illustration images, the experiment innovatively combines the attention mechanism with the cycle consistency adversarial network and proposes an efficient style transfer method for illustration images. The model comprehensively utilizes the image restoration and style transfer capabilities of the attention mechanism and the cycle consistency adversarial network, and introduces an improved attention module, which can adaptively highlight the key visual elements in the illustration, thereby maintaining artistic integrity during the style transfer process. Through a series of quantitative and qualitative experiments, high-quality style transfer is achieved, especially while retaining the original features of the illustration. The results show that when running on the Monet2photo dataset, when the system iterates to 72 times, the loss function value of the research method approaches the target value of 0.00. On the Horse2zebra dataset, as the sample size increases, the research method has the smallest FID value, and the value approaches 40.00 infinitely. With the change of peak signal-to-noise ratio, the accuracy of the research algorithm has been greater than 95.00%. Practical application found that the color of the image obtained by the research method is more gorgeous and the line features are more obvious. The above results all show that the research method has achieved more satisfactory results in the task of style transfer of illustration images, especially in terms of the accuracy of style transfer and the retention of image details.
为提高插画图像的表现力和真实感，该实验创新性地将注意力机制与循环一致对抗网络相结合，提出了一种高效的插画图像风格迁移方法。该模型综合利用注意力机制和循环一致对抗网络的图像恢复与风格迁移能力，并引入了一种改进的注意力模块，其能够自适应地突出插画中的关键视觉元素，从而在风格迁移过程中保持艺术完整性。通过一系列定量和定性实验，实现了高质量的风格迁移，尤其是在保留插画原始特征方面。结果表明，在Monet2photo数据集上运行时，当系统迭代至72次时，该研究方法的损失函数值接近目标值0.00。在Horse2zebra数据集上，随着样本数量增加，该研究方法的FID值最小，且该值无限趋近于40.00。随着峰值信噪比的变化，该研究算法的准确率一直大于95.00%。实际应用发现，该研究方法得到的图像色彩更绚丽，线条特征更明显。上述结果均表明，该研究方法在插画图像风格迁移任务中取得了较为满意的成果，尤其是在风格迁移的准确性和图像细节保留方面。
