Aiming at the problem that the current infrared and visible image fusion based on deep learning has no labels, this paper proposes an infrared and visible image fusion algorithm based on unsupervised learning. This method utilizes the characteristics of unsupervised learning, and introduces infrared image information with high gray value into the visible image to obtain the fusion image. The deep learning network proposed in this paper is composed of 6 layers of convolution blocks, and a dual attention module is also designed to make the fusion image pay more attention to the high gray value area in the infrared image. By introducing skip connections, the shallow features are fused with the deep features, so that the details of the entire fused image are richer and the appearance of halos is reduced. A large number of experimental results show that the fusion method proposed in this paper can accurately highlight the target object while maintaining the visible texture details, enhance the visual effect of the human eye, and improve the target recognition. At the same time, the quantitative experimental results show that the fusion algorithm proposed in this paper has obvious advantages in multiple indicators.
The bit error ratio (BER) and power are very hard to calculate in space optical communications, having lots of effect
factors, so need compromise to consider the complex factors. Analysis the influence to the BER such as beam drift, beam
divergence angle, communication distance, link loss, detector sensitivity etc. Propose the view of ellipse gauss beam can
inhibit beam drift by the random shock and relative motion of the optical platform and reduce the BER, and further
propose the method of calculate the transmit power using the BER. Experiments showed that it is security to use the
calculation methods of BER and power.
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