![]() Yan pointed out that a good camouflage pattern should not only be similar to the scene in color but also be in harmony with the background in texture characteristics. According to the similarity rule, the classification of image colors can obtain the main representative colors in the background image, and filling the designed digital patches can increase the similarity between the digital camouflage pattern and the original background and enhance the camouflage effect of the target. The main color extraction of background images generally adopts -means clustering method, color histogram, and other methods. The digital camouflage design mainly includes two parts: the extraction of background tone and the design of camouflage patches. Compared with the smooth edge of traditional camouflage, the digital camouflage composed of mosaics of different shapes and sizes shows better camouflage effect in major scenes. The mosaic generated based on pixels is disordered. In optical images, pixels are regarded as the smallest indivisible unit in the whole image. In addition, the results of simulation camouflage image detection in multiple scenes show that the proposed method can effectively deal with camouflage target detection on the basis of fully retaining the original background texture information and has strong camouflage concealment effect in the scene. The quantitative analysis results show that the mean heart rate of the digital camouflage pattern based on multiscene design is at least 31.0% higher than that of the original background segmentation texture, and the standard deviation index of equivalent diameter is increased by 14.9% on average. Finally, a digital camouflage pattern is obtained. The edge scatter is enriched according to the density of the patches, and the camouflage patches are filled according to the proportion of the main color of the background. Then, a biased random walk is used to outline the camouflage patches. Firstly, the original background is preprocessed, and the background texture’s direction, corner, step length, and pixel intensity difference are statistically analyzed, and the boundary probability between pixel nodes is estimated. In order to adapt to the concealment requirements under various environmental backgrounds, combined with the camouflage performance of digital camouflage and its feedback mechanism in camouflage pattern design, this paper proposed a digital camouflage pattern design method based on biased random walk. In the face of high-tech imaging reconnaissance, battlefield detection means tend to be automated and refined. Digital camouflage is a common countermeasure against military reconnaissance.
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