Efficient Face Region Occlusion Repair Based on T-GANs

In the image restoration task, generative adversarial network (GAN) demonstrates excellent performance. However, there remain significant challenges concerning task of face region inpainting. Traditional model approaches are ineffective in maintaining global consistency among facial components and recovering fine details. To address this challenge, study proposes a generation combined transformer module GAN to accurately detect missing feature parts perform effective fine-grained generation. We validate proposed using different quality evaluation methods several open-source datasets experimentally demonstrate that our outperforms other current state-of-the-art models terms generated coherent naturalness features tasks.