Multi-field Features Representation Based Colorization of Grayscale Images
LI Hong'an1, ZHENG Qiaoxue1, MA Tian1, ZHANG Jing1, LI Zhanli1, KANG Baosheng2
1.College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054; 2.School of Information Science and Technology, Northwest University, Xi'an 710127
Abstract：Image colorization improves image quality by predicting color information of gray-scale images. Although the grayscale images can be colored automatically by deep learning methods, the colorization quality of targets with different scales in the images is not satifactory. Especially, the existing colorizing methods is confronted with problems of color overflow, mis-coloring and inconsistent image colors, while dealing with complex objects and small target objects. To address these problems, a method for image colorization of multi-field features representation is proposed in the paper. Firstly, the multi-field feature representation block(MFRB) is designed and combined with the upgraded U-Net to acquire multi-field feature representation U-Net. Then, a grayscale image is input into the U-Net and the color image is obtained by adversarial training with PatchGAN. Finally, the VGG-19 network is employed to compute the perceptual loss of pictures at different scales to enhance the general consistency of the image colorization results. Experimental results on six distinct datasets demonstrate that the proposed method successfully enhances the quality of colorized images and creates color images with richer colors and more consistent tones. The results of the proposed method outperform the main colorization algorithms in both quantitative assessment and subjective perception.
李洪安, 郑峭雪, 马天, 张婧, 李占利, 康宝生. 多视野特征表示的灰度图像彩色化方法[J]. 模式识别与人工智能, 2022, 35(7): 637-648.
LI Hong'an, ZHENG Qiaoxue, MA Tian, ZHANG Jing, LI Zhanli, KANG Baosheng. Multi-field Features Representation Based Colorization of Grayscale Images. Pattern Recognition and Artificial Intelligence, 2022, 35(7): 637-648.
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2022, Vol. 35 
