【摘要】 图像彩色化是指预测灰度图像的颜色信息，虽然使用深度学习方法可自动地对灰度图像彩色化，但对图像中不同尺度目标的彩色化质量不高，尤其是在对复杂物体和小目标物体彩色化时，存在颜色溢出、误着色和图像颜色不一致的问题.针对上述问题，文中提出多视野特征表示的灰度图像彩色化方法.首先，设计多视野特征表示模块(Multi-field Feature Represented Block, MFRB),与改进的U-Net结合得到多视野特征表示U-Net.然后，将灰度图像输入U-Net中，并通过与判别器的对抗训练得到彩色图像.最后，利用VGG-19网络在不同尺度上计算图像的感知损失，提高图像彩色化结果的整体一致性.在不同类别的6个数据集上的实验表明，文中方法能有效提高彩色化图像质量，产生颜色更丰富、色调更一致的彩色图像，并在客观评价指标和主观感受上都较优.更多还原

【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.更多还原