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首页> 外文期刊>Neurocomputing >Context-aware colorization of gray-scale images utilizing a cycle-consistent generative adversarial network architecture
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Context-aware colorization of gray-scale images utilizing a cycle-consistent generative adversarial network architecture

机译：使用循环一致的生成对抗网络架构的灰度图像的背景感知着色

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Converting gray-scale images to colorful ones is one of the challenging tasks in the Computer Vision area, and various approaches based on neural network architectures have been proposed to generate colorful images. However, most of the suggested colorization frameworks use a single model for colorization regardless of the diversity of colors in images of various datasets. We claim that since such a structure is responsible for generating all types of images, it results in producing either faint-colored images or some artifacts in the images. We addressed this issue by proposing parallel colorization models, each of which is customized for generating images with similar contexts or color themes. Our proposed architecture consists of two stages as follows. The first stage includes a single colorization network that generates an initial colored image and simultaneously selects one of the specialist networks of the second stage for each image. The second stage consists of several specialist networks, whose tasks are improving the quality of the initial colored image and produce real-looking, plausible images. Our goal is to assign images to specialist networks in a way that each one of the specialist networks deals with contextually similar images so that the networks become more confident in generating vibrant colors and high-quality images. Cycle-Consistent Generative Adversarial Network structure is utilized in our architecture since it has demonstrated great potential in creating plausible images in the literature. The proposed model is evaluated on the ImageNet dataset, and the results stand for the effectiveness of employing context-aware specialists for colorization. (c) 2020ElsevierB.V. Allrightsreserved.
机译：将灰度图像转换为彩色的图像是计算机视觉区域的具有挑战性的任务之一，并提出了基于神经网络架构的各种方法来生成彩色图像。然而，大多数建议的着色框架使用单个模型进行着色，而不管各个数据集的图像中的颜色的分集。我们声称，由于这种结构负责生成所有类型的图像，因此它导致在图像中产生微弱的图像或一些伪像。我们通过提出并行着色模型来解决这个问题，每个模型都是为生成具有类似上下文或颜色主题的图像而定制的。我们所提出的架构由两个阶段组成，如下所示。第一阶段包括单个彩色网络，其产生初始彩色图像，并同时为每个图像选择第二级的一个专家网络。第二阶段由若干专业网络组成，其任务正在提高初始彩色图像的质量，并产生真实的合理图像。我们的目标是以每个专业网络处理上下文类似的图像的方式为专业网络分配图像，使得网络在产生充满活力和高质量图像时变得更加自信。在我们的架构中使用循环一致的生成对抗网络结构，因为它在文献中创建合理图像的巨大潜力。所提出的模型是在想象的数据集上进行评估，结果代表使用上下文感知专家进行着色。 （c）2020elsevierb.v。版权所有。

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