放弃拜耳滤波器，在黑暗中看
Abandoning the Bayer-Filter to See in the Dark
摘要：
弱光图像增强是一个普遍但具有挑战性的问题，在增强弱光照环境下捕获的图像的可见性方面起着核心作用。由于并非所有光子都可以通过彩色相机传感器上的拜耳滤波器，因此在这项工作中，我们首先提出了一个基于深度神经网络的De-Bayer滤波器模拟器，以从彩色原始图像生成单色原始图像。接下来，提出了一种全卷积网络，通过将彩色原始数据与合成的单色原始数据融合来实现弱光图像增强。智能注意力也被引入到融合过程中，以建立彩色和单色原始图像的功能之间的互补互动。为了训练卷积网络，我们提出了一个由单色和彩色原始数据对组成的数据集，称为单色原始数据对数据集（MCR），该数据集是通过使用不带拜耳滤波器的单色相机和带拜耳滤波器的彩色相机收集的。所提出的流水线利用了虚拟单色和彩色原始图像的融合，我们的广泛实验表明，通过利用原始传感器数据和数据驱动学习可以实现显着的改进。
英文摘要：
Low-light image enhancement - a pervasive but challenging problem, plays a central role in enhancing the visibility of an image captured in a poor illumination environment. Due to the fact that not all photons can pass the Bayer-Filter on the sensor of the color camera, in this work, we first present a De-Bayer-Filter simulator based on deep neural networks to generate a monochrome raw image from the colored raw image. Next, a fully convolutional network is proposed to achieve the low-light image enhancement by fusing colored raw data with synthesized monochrome raw data. Channel-wise attention is also introduced to the fusion process to establish a complementary interaction between features from colored and monochrome raw images. To train the convolutional networks, we propose a dataset with monochrome and color raw pairs named Mono-Colored Raw paired dataset (MCR) collected by using a monochrome camera without Bayer-Filter and a color camera with Bayer-Filter. The proposed pipeline take advantages of the fusion of the virtual monochrome and the color raw images and our extensive experiments indicate that significant improvement can be achieved by leveraging raw sensor data and data-driven learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
