Two-stage pyramidal convolutional neural networks for image colorization

IF 3.2 Q1 Computer Science
Yu-Jen Wei, Tsu-Tsai Wei, Tien-Ying Kuo, Po-Chyi Su
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引用次数: 1

Abstract

The development of colorization algorithms through deep learning has become the current research trend. These algorithms colorize grayscale images automatically and quickly, but the colors produced are usually subdued and have low saturation. This research addresses this issue of existing algorithms by presenting a two-stage convolutional neural network (CNN) structure with the first and second stages being a chroma map generation network and a refinement network, respectively. To begin, we convert the color space of an image from RGB to HSV to predict its low-resolution chroma components and therefore reduce the computational complexity. Following that, the first-stage output is zoomed in and its detail is enhanced with a pyramidal CNN, resulting in a colorized image. Experiments show that, while using fewer parameters, our methodology produces results with more realistic color and higher saturation than existing methods.
用于图像着色的两阶段金字塔卷积神经网络
通过深度学习开发着色算法已成为当前的研究趋势。这些算法自动快速地对灰度图像上色,但产生的颜色通常较弱,饱和度较低。本研究通过提出一种两阶段卷积神经网络(CNN)结构来解决现有算法的这一问题,第一阶段和第二阶段分别是色度图生成网络和细化网络。首先,我们将图像的颜色空间从RGB转换为HSV,以预测其低分辨率色度成分,从而降低计算复杂度。接下来,将第一阶段的输出放大,并使用金字塔形CNN增强其细节,从而得到彩色图像。实验表明,在使用更少参数的情况下,我们的方法比现有方法产生更真实的颜色和更高的饱和度。
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
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