Digital Camouflage Generation Based on an Improved CycleGAN Network Model

This paper proposes a digital camouflage generation method based on an improved CycleGAN to produce camouflage patterns with a high degree of fusion with the background and realistic texture details. Firstly, a SE-ResNet network structure is constructed by combining the residual network ResNet with the channel attention mechanism SENet, enabling flexible adjustment of channel weights to effectively extract crucial channel features and enhance the network's perception capability of important information in images. Secondly, a color preservation loss is introduced to improve the adversarial loss function, thereby avoiding training instability and fluctuation in pattern quality. Experimental results demonstrate that the camouflage patterns generated using the proposed method achieve a Structural Similarity Index (SSIM) of 0.77 and a Peak Signal-to-Noise Ratio (PSNR) of 18.9, representing improvements of 0.27 and 3.3, respectively, compared to the original CycleGAN. This method can generate digital camouflage patterns with richer details, textures, and high fusion with the background.