论文标题
跨摄像机深色
Cross-Camera Deep Colorization
论文作者
论文摘要
在本文中，我们考虑了颜色加式双相机系统，并提出了一个端到端卷积神经网络，以有效且具有成本效益的方式使图像对齐和融合图像。我们的方法将跨域和跨尺度图像作为输入，因此综合了HR着色结果，以促进单相机成像系统中的时空分辨率和色深度深度之间的权衡。与以前的着色方法相反，我们的功能可以适应具有独特时空分辨率的颜色和单色相机，从而在实际应用中具有灵活性和鲁棒性。我们方法的关键成分是跨相机比对模块，该模块生成跨域图像对齐的多尺度对应关系。通过在各种数据集和多个设置上进行大量实验，我们验证了方法的灵活性和有效性。值得注意的是，我们的方法始终取得了实质性改进，即在最新方法上，大约10dB PSNR增益。代码为：https：//github.com/indigopurple/ccdc
In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align and fuse images from it in an efficient and cost-effective way. Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system. In contrast to the previous colorization methods, ours can adapt to color and monochrome cameras with distinctive spatial-temporal resolutions, rendering the flexibility and robustness in practical applications. The key ingredient of our method is a cross-camera alignment module that generates multi-scale correspondences for cross-domain image alignment. Through extensive experiments on various datasets and multiple settings, we validate the flexibility and effectiveness of our approach. Remarkably, our method consistently achieves substantial improvements, i.e., around 10dB PSNR gain, upon the state-of-the-art methods. Code is at: https://github.com/IndigoPurple/CCDC
