Division-of-focal-plane (DoFP) polarization imaging technical recently has been applied in many fields. However, the images captured by such sensors cannot be used directly because they suffer from instantaneous field-of-view errors and low resolution problem. This paper builds a fast DoFP demosaicing system with proposed progressive polarization demosaicing convolutional neural network (PPDN), which is specifically designed for edge-side GPU devices like Navidia Jetson TX2. The proposed network consists of two parts: reconstruction stage and refining stage. The former recovers four polarization channels from a single DoFP image. The latter fine-tune the four channels to obtain more accurate polarization information. PPDN can be implemented in another version: PPDN-L (large), for the platforms of high computing resources. Experiments show that PPDN can compete with the best existing methods with fewer parameters and faster inference speed and meet the real-time demands of imaging system.
翻译:最近,在很多领域应用了分层极化成像技术(DoFP),但无法直接使用这些传感器所摄取的图像,因为这些图像有瞬时的视野错误和低分辨率问题。本文建立了一个快速的DFP演示系统,配有拟议的渐进分层分层分解神经神经神经网络(PPDN),该系统专门为Navidia Jetson TX2等边缘的GPU装置设计。拟议网络由两部分组成:重建阶段和精炼阶段。前者从一个多FP图像中回收了四个两极化通道。后者微调了四个通道以获取更准确的极化信息。PPDN-L(大)可用于高计算资源平台。实验显示,PPDN可以与现有最佳方法竞争,其参数更少,引力更快,并满足成像系统的实时需求。