Near-Infrared Image Colorization with Weighted UNet++ and Auxiliary Color Enhancement GAN
Near-Infrared Image Colorization with Weighted UNet++ and Auxiliary Color Enhancement GAN
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DOI：
10.1109/icivc55077.2022.9887040
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Sicong Zhou;S. Kamata
中科院分区：
文献类型：
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作者：
Sicong Zhou;S. Kamata
提出了一种新的基于gan的近红外图像着色方法。该方法创新性地通过引入亮度通道和特征权重驱动的颜色生成器来重新平衡着色图像的颜色。我们在生成器中设置了加权UNet++结构，用于带有焦点物体细节的着色结果。采用由更深亮度网络和比色网络组成的色彩增强网络进行全局色彩平衡，以提高生成的彩色图像的色彩质量。我们的网络在两个数据集上进行训练和评估。根据FID， SSIM和PSNR结果，我们的网络表现良好，对整体颜色和细节颜色都有良好的恢复效果，优于当前最先进的方法。
We propose a novel GAN-based method for near-infrared image colorization. This method innovatively rebalances the color of the colorization image by importing a luminance channel and a feature weight-driven color generator. We set the weighted UNet++ structure in the generator for colorization results with the detail of focal objects. A color enhancement network composed of a deeper luminance network and a colorimetric network is used for global color balance to improve the color quality of the generated color images. Our network is trained and evaluated on two datasets. According to the FID, SSIM and PSNR results, our network performs well, with good recovery effects for both overall color and detailed color and outperforming the current state-of-the-art methods.