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Pixel-wise operations between polarimetric images are important forprocessing polarization information. For the lack of such operations, thepolarization information cannot be fully utilized in convolutional neuralnetwork(CNN). In this paper, a novel end-to-end CNN framework for polarizationvision tasks is proposed, which enables the networks to take full advantage ofpolarimetric images. The framework consists of two sub-networks: apolarization-parameter-constructing network (PPCN) and a task network. PPCNimplements pixel-wise operations between images in the CNN form with 1x1convolution kernels. It takes raw polarimetric images as input, and outputspolarization-parametric images to task network so as to complete a vison task.By training together, the PPCN can learn to provide the most suitablepolarization-parametric images for the task network and the dataset. Takingfaster R-CNN as task network, the experimental results show that compared withexisting methods, the proposed framework achieves much highermean-average-precision (mAP) in object detection task

An end-to-end CNN framework for polarimetric vision tasks based on polarization-parameter-constructing network