Image Restoration Based on Generative Adversarial Networks with Hybrid Attention Mechanisms

Image restoration is a very important task that aims to recover a complete image from a damaged or missing one.In light of the prevalent shortcomings of current algorithms for recovering large missing images, characterized by the presence of blurred texture details and distorted structural elements, this research paper proposes a novel approach that utilizes a generative adversarial network(GAN) in conjunction with a hybrid attention mechanism. This combined framework aims to enhance both the clarity of detailed image information and the overall structural integrity.Additionally, a dual-discriminator is employed to collectively evaluate the restoration results on both a global and local scale, providing guidance to the generator to produce restoration results that are "fake-to-real." Experiments using the Places365-Standard scene dataset and the CelebA-HQ HD face dataset to train and validate the model . Experiments show that the algorithm in this paper can effectively improve the quality of restoration results for large missing images.