Low-light imaging is a challenging task because of the excessive photon shot noise. Color imaging in low-light is even more difficult because one needs to demosaick and denoise simultaneously. Existing demosaicking algorithms are mostly designed for well-illuminated scenarios, which fail to work with low-light. Recognizing the recent development of small pixels and low read noise image sensors, we propose a learning-based joint demosaicking and denoising algorithm for low-light color imaging. Our method combines the classical theory of color filter arrays and modern deep learning. We use an explicit carrier to demodulate the color from the input Bayer pattern image. We integrate trainable filters into the demodulation scheme to improve flexibility. We introduce a guided filtering module to transfer knowledge from the luma channel to the chroma channels, thus offering substantially more reliable denoising. Extensive experiments are performed to evaluate the performance of the proposed method, using both synthetic datasets and real data. Results indicate that the proposed method offers consistently better performance over the current state-of-the-art, across several standard evaluation metrics.

中文翻译：

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使用学习频率选择对小像素进行低光去马赛克和去噪


由于光子散粒噪声过多，低光成像是一项具有挑战性的任务。低光下的彩色成像更加困难，因为需要同时进行去马赛克和去噪。现有的去马赛克算法大多是针对照明良好的场景而设计的，无法在弱光下工作。认识到小像素和低读取噪声图像传感器的最新发展，我们提出了一种用于低光彩色成像的基于学习的联合去马赛克和去噪算法。我们的方法结合了滤色器阵列的经典理论和现代深度学习。我们使用显式载波从输入拜耳模式图像中解调颜色。我们将可训练滤波器集成到解调方案中以提高灵活性。我们引入了引导滤波模块，将知识从亮度通道传输到色度通道，从而提供更加可靠的去噪。使用合成数据集和真实数据进行了大量的实验来评估所提出方法的性能。结果表明，所提出的方法在多个标准评估指标上始终提供优于当前最先进技术的性能。