Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. This paper presents a compact demosaicking neural network based on the UNet++ structure. The network inserts densely connected layer blocks and adopts Gaussian smoothing layers instead of down-sampling operations before the backbone network. The densely connected blocks can extract mosaic image features efficiently by utilizing the correlation between feature maps. Furthermore, the block adopts depthwise separable convolutions to reduce the model parameters; the Gaussian smoothing layer can expand the receptive fields without down-sampling image size and discarding image information. The size constraints on the input and output images can also be relaxed, and the quality of demosaicked images is improved. Experiment results show that the proposed network can improve the running speed by 42% compared with the fastest CNN-based method and achieve comparable reconstruction quality as it on four mainstream datasets. Besides, when we carry out the inference processing on the demosaicked images on typical deep CNN networks, Mobilenet v1 and SSD, the accuracy can also achieve 85.83% (top 5) and 75.44% (mAP), which performs comparably to the existing methods. The proposed network has the highest computing efficiency and lowest parameter number through all methods, demonstrating that it is well suitable for applications on modern edge computing devices.

中文翻译：

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用于边缘计算设备的紧凑型高质量图像去马赛克神经网络

在图像传感器背后最重要的图像处理步骤中，图像去马赛克一直是一个至关重要且具有挑战性的问题。由于基于深度学习的智能处理器的快速发展，提出了几种基于卷积神经网络（CNN）的去马赛克方法。但是，它们的网络很难在具有大量模型参数的边缘计算设备上实时运行。本文提出了一种基于UNet ++结构的紧凑型去马赛克神经网络。该网络插入密集连接的层块，并在主干网络之前采用高斯平滑层，而不是下采样操作。通过利用特征图之间的相关性，紧密连接的块可以有效地提取马赛克图像特征。此外，块采用深度可分离卷积来减少模型参数；高斯平滑层可以扩展接收场，而无需下采样图像大小和丢弃图像信息。还可以放宽对输入和输出图像的尺寸限制，并提高去马赛克图像的质量。实验结果表明，与基于最快的CNN的方法相比，所提出的网络可以将运行速度提高42％，并且可以在四个主流数据集上达到可比的重建质量。此外，当我们在典型的深层CNN网络Mobilenet v1和SSD上对去马赛克图像进行推理处理时，其准确度也可以达到85.83％（前5名）和75.44％（mAP），与现有方法相比具有相当的优势。