Although artists’ actions in photo retouching appear to be highly nonlinear in nature and very difficult to characterize analytically, we find that the net effects of interactively editing a mundane image to a desired appearance can be modeled, in most cases, by a parametric monotonically non-decreasing global tone mapping function in the luminance axis and by a global affine transform in the chrominance plane that are weighted by saliency. This allows us to simplify the machine learning problem of mimicking artists in photo retouching to constructing a deep artful image transform (DAIT) using convolutional neural networks (CNN). The CNN design of DAIT aims to learn the image-dependent parameters of the luminance tone mapping function and the affine chrominance transform, rather than learning the end-to-end pixel level mapping as in the mainstream methods of image restoration and enhancement. The proposed DAIT approach reduces the computation complexity of the neural network by two orders of magnitude, which also, as a side benefit, improves the robustness and generalization capability at the inference stage. The high throughput and robustness of DAIT lend itself readily to real-time video enhancement as well after a simple temporal processing. Experiments and a Turing-type test are conducted to evaluate the proposed method and its competitors.

中文翻译：

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基于学习语义相关全局变换的实时深度图像修饰

尽管艺术家在照片润饰中的行为在本质上似乎是高度非线性的，并且很难通过分析来表征，但我们发现，在大多数情况下，可以通过参数化单调非参数化将普通图像交互编辑为所需外观的净效果进行建模。 - 降低亮度轴中的全局色调映射函数，并通过显着性加权的色度平面中的全局仿射变换。这使我们能够将在照片修饰中模仿艺术家的机器学习问题简化为使用卷积神经网络 (CNN) 构建深度艺术图像变换 (DAIT)。DAIT 的 CNN 设计旨在学习亮度色调映射函数和仿射色度变换的图像相关参数，而不是像在图像恢复和增强的主流方法中那样学习端到端的像素级映射。所提出的 DAIT 方法将神经网络的计算复杂度降低了两个数量级，这也作为附带好处，提高了推理阶段的鲁棒性和泛化能力。DAIT 的高吞吐量和稳健性使其易于实时视频增强以及经过简单的时间处理。进行实验和图灵型测试以评估所提出的方法及其竞争对手。DAIT 的高吞吐量和稳健性使其易于实时视频增强以及经过简单的时间处理。进行实验和图灵型测试以评估所提出的方法及其竞争对手。DAIT 的高吞吐量和稳健性使其易于实时视频增强以及经过简单的时间处理。进行实验和图灵型测试以评估所提出的方法及其竞争对手。