In digital imaging, image demosaicing is a crucial first step which recovers the RGB information from a color filter array (CFA). Oftentimes, deep learning is utilized to perform image demosaicing. Given that most modern digital imaging applications occur on mobile platforms, applying deep learning to demosaicing requires lightweight and efficient networks. Isotropic networks, also known as residual-in-residual networks, have been often employed for image demosaicing and joint-demosaicing-and-denoising (JDD). Most demosaicing isotropic networks avoid spatial downsampling entirely, and thus are often prohibitively expensive computationally for mobile applications. Contrary to previous isotropic network designs, this paper claims that spatial downsampling to a signficant degree can improve the efficiency and performance of isotropic networks. To validate this claim, we design simple fully convolutional networks with and without downsampling using a mathematical architecture design technique adapted from DeepMAD, and find that downsampling improves empirical performance. Additionally, empirical testing of the downsampled variant, JD3Net, of our fully convolutional networks reveals strong empirical performance on a variety of image demosaicing and JDD tasks.

翻译：在数字成像领域，图像去马赛克是从彩色滤波阵列中恢复RGB信息的关键初始步骤。深度学习常被用于执行图像去马赛克任务。鉴于大多数现代数字成像应用运行于移动平台，将深度学习应用于去马赛克需要轻量且高效的网络。各向同性网络，亦称残差嵌套网络，已广泛应用于图像去马赛克及联合去马赛克与去噪任务。大多数去马赛克各向同性网络完全避免空间下采样，因而其计算成本对移动应用而言往往过高。与先前各向同性网络设计不同，本文主张一定程度的空间下采样能够提升各向同性网络的效率与性能。为验证此主张，我们采用源自DeepMAD的数学架构设计技术，构建了包含下采样与不包含下采样的简单全卷积网络，并发现下采样能提升实证性能。此外，对我们全卷积网络的下采样变体JD3Net进行的实证测试表明，其在多种图像去马赛克及联合去马赛克与去噪任务中均展现出优异的实证性能。