学习用于图像修饰的多种色调风格.
概括
本研究介绍了一种基于流量的新规范化方法,用于各种图像修饰,解决确定性模型的局限性. 这种方法有效地将风格与内容分开,产生各种修饰结果以满足用户的美学偏好.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 图像修饰是主观的,用户有不同的美学偏好.
- 现有的决定性模型很难捕捉到这种多样性和专家的变化.
研究的目的:
- 开发一种灵活的图像修饰方法,能够产生多样化的结果.
- 解决确定性模型在捕捉主观美学偏好方面的局限性.
主要方法:
- 提出了一种基于流程的规范化架构,用于各种图像修饰.
- 引入了一种联合训练管道,其中包括风格编码器,条件RetouchNet和图像色调风格规范化流 (TSFlow).
- 在一维的风格空间中学习了修饰风格,以将风格与内容分开,避免空间不和.
主要成果:
- 该TSFlow模块有效地将样式表示映射到高斯分布中.
- 通过从学习分布中抽取样本,生成各种图像色调风格向量.
- 在 MIT-Adobe FiveK 和 PPR10K 数据集上与最先进的方法相比,实现了有利的性能.
结论:
- 提出的方法有效地产生了各种各样的图像修饰结果.
- 成功地满足了各种各样的人类美学偏好.
- 展示了学习在一维风格空间中的优势,用于图像修饰.
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