A small-spot deformation camouflage design algorithm based on background texture matching

In order to solve the problem of poor fusion between the spots of deformation camouflage and the background, a small-spot deformation camouflage design algorithm based on background texture matching is proposed in this research. The combination of spots and textures improved the fusion of the spot pattern and the background. An adversarial autoencoder convolutional network was designed to extract background texture features. The image adversarial loss was added and the reconstruction loss was improved to improve the clarity of the generated texture pattern and the generalization ability of the model. The digital camouflage was formed by obtaining the mean value of the square area and replacing the main color. At the same time, the spots in the square area with a side length of 2 s were subjected to simple linear iterative clustering to form irregular small-spot camouflage. A dataset with a scale of 1050 was established in the experiment. The training results of three different loss functions were investigated. The results showed that the proposed loss function could enhance the generalization of the model and improve the quality of the generated texture image. A variety of digital camouflages with main colors and irregular small-spot camouflage were generated, and their efficiency was tested. On the one hand, intuitive evaluation was given by personnel observing the camouflage pattern embedded in the background and its contour map calculated by the canny operator. On the other hand, objective comparison result was formed by calculating the 4 evaluation indexes between the camouflage pattern and the background. Both results showed that the generated pattern had a high degree of fusion with the background. This model could balance the relationship between the spot size, the number of main colors and the actual effect according to actual needs.