Improving Image Inpainting with Emphasis on Texture, Structure and Semantic Features Using BioInspired Generative Adversarial Network

Generative models have recently produced good results in image inpainting. Generally, structure, texture, and semantics are essential aspects for an image to be visually appealing. Most of the existing methods fail to consider all these three aspects, producing unwanted artifacts and fuzzy textures, especially for images with large damaged regions. Being an ill posed inverse problem, it is appreciable to solve the problem of inpainting from the perception of prior information. So, the proposed work suggests an inpainting framework named TSSI-GAN: Texture, Structure, and Semantic-based Inpainting - Generative Adversarial Network, which aims to extract the texture, structure, and semantic context of the degraded input image and adopt this knowledge as priors to perform the inpainting process. To obtain photo-realistic images as output, the optimization of the discriminator used in the inpainting module is an essential factor. So, the presented work introduces a novel and persuasive classification approach named Dolphin-Chicken Swarm Optimizer (DCSO), which is used to optimize the discriminator of the inpainting module. The usage of DCSO enhances the inpainting process to produce optimal results such that the inpainted images look visually appealing, and also the similarity between the ground truth and inpainted images is highly appreciable. The proposed DCSO incorporates the Dolphin EchoLocation (DEL) Algorithm with the chicken swarm behavior of the Chicken Swarm Optimization (CSO) algorithm. The proposed framework is evaluated using Flickr Faces-HQ (FFHQ) and DIV2K datasets and the results prove that the suggested framework excels the state-of-the-art models.