Three-dimensional polarimetric image restoration in low light with deep residual learning and integral imaging

Polarimetric imaging can become challenging in degraded environments such as low light illumination conditions or in partial occlusions. In this paper, we propose the denoising convolutional neural network (DnCNN) model with three-dimensional (3D) integral imaging to enhance the reconstructed image quality of polarimetric imaging in degraded environments such as low light and partial occlusions. The DnCNN is trained based on the physical model of the image capture in degraded environments to enhance the visualization of polarimetric imaging where simulated low light polarimetric images are used in the training process. The DnCNN model is experimentally tested on real polarimetric images captured in real low light environments and in partial occlusion. The performance of DnCNN model is compared with that of total variation denoising. Experimental results demonstrate that DnCNN performs better than total variation denoising for polarimetric integral imaging in terms of signal-to-noise ratio and structural similarity index measure in low light environments as well as low light environments under partial occlusions. To the best of our knowledge, this is the first report of polarimetric 3D object visualization and restoration in low light environments and occlusions using DnCNN with integral imaging. The proposed approach is also useful for 3D image restoration in conventional (non-polarimetric) integral imaging in a degraded environment.