Image Segmentation in Complex Backgrounds using an Improved Generative Adversarial Network

Abstract

As technologyadvances, solving image segmentation challenges in complex backgrounds has become a key issue across various fields. Traditional image segmentation methods underperform in addressing these challenges, and existing generative adversarial networks (GANs) also face several problems when applied in complex environments, such as low generation quality and unstable model training. To address these issues, this study introduces an improved GAN approach for image segmentation in complex backgrounds. This method encompasses preprocessing of complex background image datasets, feature reduction encoding based on cerebellar neural networks, image data augmentation in complex backgrounds, and the application of an improved GAN. In this paper, new generator and discriminator network structures are designed and image data enhancement is implemented through self-play learning. Experimental results demonstrate significant improvements in image segmentation tasks in various complex backgrounds, enhancing the accuracy and robustness of segmentation. This research offers new insights and methodologies for image processing in complex backgrounds, holding substantial theoretical and practical significance.