Self-Supervised Colorization towards Monochrome-Color Camera Systems Using Cycle CNN

Colorization in monochrome-color camera systems aims to colorize the gray image ${{\mathrm{I}}_{\mathrm{G}}}$ from the monochrome camera using the color image ${{\mathrm{R}}_{\mathrm{C}}}$ from the color camera as reference. Since monochrome cameras have better imaging quality than color cameras, the colorization can help obtain higher quality color images. Related learning based methods usually simulate the monochrome-color camera systems to generate the synthesized data for training, due to the lack of ground-truth color information of the gray image in the real data. However, the methods that are trained relying on the synthesized data may get poor results when colorizing real data, because the synthesized data may deviate from the real data. We present a self-supervised CNN model, named Cycle CNN, which can directly use the real data from monochrome-color camera systems for training. In detail, we use the Weighted Average Colorization (WAC) network to do the colorization twice. First, we colorize ${{\mathrm{I}}_{\mathrm{G}}}$ using ${{\mathrm{R}}_{\mathrm{C}}}$ as reference to obtain the first-time colorization result ${{\mathrm{I}}_{\mathrm{C}}}$. Second, we colorize the de-colored map of ${{\mathrm{R}}_{\mathrm{C}}}$ , i.e. ${{\mathrm{R}}_{\mathrm{G}}}$ , using the concatenated image of ${{\mathrm{I}}_{\mathrm{G}}}$ and Cb/Cr channels of the first-time colorization result ${{\mathrm{I}}_{\mathrm{C}}}$ , i.e. ${{\mathrm{I}}_{\mathrm{C}}^{Cb}}$ and ${{\mathrm{I}}_{\mathrm{C}}^{Cr}}$ , as reference to obtain the second-time colorization result ${\mathrm{R}}_{\mathrm{C}}^{{ {'}}}$. In this way, for the second-time colorization result ${\mathrm{R}}_{\mathrm{C}}^{{ {'}}}$ , we use the Cb and Cr channels of the original color map ${{\mathrm{R}}_{\mathrm{C}}}$ as ground-truth and introduce the cycle consistency loss to push ${\mathrm{R}}_{\mathrm{C}}^{{ {'}}Cb/Cr} \approx {\mathrm{R}}_{\mathrm{C}}^{Cb/Cr}$. Also, for the $Y$ channel of the first-time colorization result ${{\mathrm{I}}_{\mathrm{C}}^{Y}}$ , we propose the Global Curve Adjustment (GCA) network and the structure similarity loss to encourage the structure similarity between ${{\mathrm{I}}_{\mathrm{C}}^{Y}}$ and ${{\mathrm{I}}_{\mathrm{G}}}$. In addition, we introduce a spatial smoothness loss within the WAC network to encourage spatial smoothness of the colorization result. Combining all these losses, we could train the Cycle CNN using the real data in the absence of the ground-truth color information of ${{\mathrm{I}}_{\mathrm{G}}}$. Experimental results show that we can outperform related methods largely for colorizing real data.