论文标题
放弃拜耳过滤器，在黑暗中看到
Abandoning the Bayer-Filter to See in the Dark
论文作者
论文摘要
低光图像增强 - 一个普遍但具有挑战性的问题，在增强在较差的照明环境中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摄像机传感器上的拜耳过滤器传递拜耳过滤器，因此在这项工作中，我们首先提出一个基于深神经网络的脱毛器滤波器模拟器，以从彩色原始图像中生成单色原始图像。接下来，提出了一个完全卷积的网络，以通过将彩色原始数据与合成的单色原始数据融合来实现低光图像增强。融合过程还引入了频道的注意力，以在彩色和单色原始图像中建立互补的相互作用。为了训练卷积网络，我们通过使用没有拜耳过滤器的单色摄像头和带有拜耳过滤器的彩色摄像头提出了一个带有单色和彩色生对的数据集，该数据集名为单色原始配对数据集（MCR）。所提出的管道利用了虚拟单色和颜色原始图像的融合的优势，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的学习，可以实现显着改进。
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.
