GAN for Semantic Image Synthesis With Laplacian Pyramid and Multi-Scale Channel Attention

Most GAN-based methods utilize semantic layouts as input for generating realistic images. However, these layouts primarily consist of object contours and often lack detailed information, leading to suboptimal image quality in the generated outputs. To address this limitation, we propose a novel GAN architecture called LMCGAN designed specifically for synthesizing high-quality images. LMCGAN introduces a generator network structured around the laplacian pyramid, enabling the simultaneous generation of multi-scale feature maps.This approach allows the model to capture finer details at different resolutions, enhancing the overall realism of the generated images.To further improve the utilization of semantic maps, we integrate a multi-scale channel attention (MSCA) mechanism.This mechanism effectively focuses on channel-specific information in complex scenes, which is crucial for preserving essential details that may otherwise be lost. During the feature fusion phase, we implement a feature fusion block (FFBL) that is designed to capture important relationships across various scales. This block facilitates the integration of information from different resolutions, ensuring that the final output retains critical features. Additionally, we adopt a combination of conditional and unconditional methods to reduce noise during the training process, leading to more stable and effective training dynamics. Extensive experiments conducted on challenging datasets demonstrate that LMCGAN significantly outperforms existing methods in terms of both visual quality and quantitative evaluation metrics. The results indicate that our architecture not only generates more realistic images but also excels in preserving intricate details, marking a substantial advancement in the field of image synthesis using GANs.