Real-time division-of-focal-plane polarization imaging system with progressive networks
This work addresses real-time polarization imaging for edge-side GPU devices, representing an incremental improvement in demosaicing methods.
The paper tackled the problem of low-resolution and field-of-view errors in division-of-focal-plane polarization imaging by proposing a progressive convolutional neural network (PPDN) for demosaicing, achieving competitive performance with fewer parameters and faster inference speed to meet real-time demands.
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.