Author(s): Wang, SY (Wang, Shuyu); Zhao, MX (Zhao, Mingxin); Dou, RJ (Dou, Runjiang); Yu, SM (Yu, Shuangming); Liu, LY (Liu, Liyuan); Wu, NJ (Wu, Nanjian)

Source: SENSORS Volume: 21 Issue: 9 Article Number: 3265 DOI: 10.3390/s21093265 Published: MAY 2021

Abstract: 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.

Accession Number: WOS:000650771800001

eISSN: 1424-8220

Full Text: https://www.mdpi.com/1424-8220/21/9/3265