Adaptive perception enhancement-based virtual try-on technology for accessories with the assistance of artificial intelligence

The image quality generated by the traditional virtual try-on technique for non-heritage accessories is poor, to address this problem, this paper firstly designs a convolutional neural network that adaptively adjusts the feature extraction strategy, and adopts an improved generative adversarial network to generate a primary feature map. Then the background noise of the primary feature map is suppressed based on multi-scale attention, and an adaptive perceptual enhancement module is designed to weight the features at different locations in the feature map to strengthen the representation of important features. Finally, the primary feature maps are perspective corrected and downscaled using multi-scale weights to enable the network to generate high-quality images of non-heritage accessory try-on. Experimental results on UNESCO and VITON datasets show that the structural similarity (SSIM) of the suggested method improves 3.45-26.76% compared to benchmark methods, which can effectively improve the quality of image generation.