Mantis shrimp have complex visual sensors, and thus, they have both color vision and polarization vision, and are adept at using polarization information for visual tasks, such as finding prey. In addition, mantis shrimp, almost unique among animals, can perform three-axis eye movements, such as pitch, yaw, and roll. With this behavior, polarization contrast in their field of view can be adjusted in real time. Inspired by this, we propose a bionic model that can adaptively enhance contrast vision. In this model, a pixel array is used to simulate a compound eye array, and the angle of polarization (AoP) is used as an adjustment mechanism. The polarization information is pre-processed by adjusting the direction of the photosensitive axis point-to-point. Experiments were performed around scenes where the color of the target and the background were similar, or the visibility of the target was low. The influence of the pre-processing model on traditional feature components of polarized light was analyzed. The results show that the model can effectively improve the contrast between the object and the background in the AoP image, enhance the significance of the object, and have important research significance for applications, such as contrast-based object detection.

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Stomatopods are creatures that have a unique ability to manipulate their environment by detecting polarized light for finding prey, choosing habitat, and navigation. In this study, based on the concept of polarization distance proposed by Martin J et al 2014 [Proc. R. Soc. B 281, 20131632], we have analyzed several multi-channel polarization distance models. The simulation and experimental results revealed that compared to other models, a four-channel polarization distance model can significantly enhance the contrast between the target and the background, and it exhibits excellent performance in terms of scene discrimination capability and robustness to noise. The structure and signal processing method of this model are inspired by biological polarization vision such as that of mantis shrimps. According to this method, a polarization-vision neural network is simulated with four-orientation receptor information as the input, and the network connections are realized in a cascaded order. The target–background contrast enhancement method based on this model has wide application prospects in the field of camouflage removal and target detection.

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