Enhanced Image Generation with MorphoGAN: Combining MNNs and GANs

In this paper, we introduce an innovative technique to enhance the performance of Generative Adversarial Networks (GANs) for image generation tasks by incorporating Morphological Neural Networks (MNNs). The primary objective of this research is to address some of the common limitations associated with GANs, such as mode collapse and training instability, by harnessing the distinctive capabilities of MNNs in analyzing and processing image structures. We put forth a methodology that integrates morphological operations, including dilation and erosion, within the generator and discriminator components of GANs. Our experiments are carried out using the CIFAR-10 data-set, and the performance of our proposed integrated model is compared with multiple established GAN variants. The experimental outcomes reveal that our approach significantly improves image quality, convergence, and stability while maintaining a high level of resistance to noise and artifacts. This study lays the groundwork for further investigation into the synergy of MNNs and GANs across a broad spectrum of image generation applications, presenting valuable avenues for future research.