Hybrid attention generative adversarial network: texture inpainting algorithm for iris defects with excellent repair performance and generalization

Iris defect texture inpainting is a challenging problem that is not only limited by a lack of research but also by requiring a higher degree of texture refinement than other types of images. In the field of image inpainting, most recent research has focused on designing improved encoder-decoder models. Solving the image blurring problem caused by autoencoder models has become a key factor in judging their merits. Generative adversarial networks were designed based on a hybrid attention generative adversarial network (Hybrid A-GAN) mechanism to repair missing iris textures. The generator is based on the encoder-decoder structure and introduces two attention mechanisms to enable obtaining the correlation between channels in the feature map and pixel importance in space, which enhances the network’s ability to utilize feature information. Moreover, the improved jump connection effectively fuses the high-level features with the low-level features after weighting, which prevents the information loss caused by the downsampling process and enhances the image generation capability. In addition, the joint Wasserstein generative adversarial network-gradient penalty and L1 loss jointly guide network training, which further enhances network generation performance and ensures global consistency of the generated images. Extensive repair experiments and recognition experiments conducted on three publicly available datasets demonstrated that hybrid A-GAN has excellent repair capability and generalization performance.