The potential limitations of RAU-Net and MSW-MSA approaches include:
Loss of Fine Details: RAU-Net's downsampling and upsampling operations may lead to a loss of fine details in the generated images, especially in the later stages of denoising. This can result in less sharp and detailed images.
Limited Contextual Information: MSW-MSA's window attention may have limitations in capturing global contextual information, which could impact the coherence and overall quality of the generated images, especially in complex scenes.
To address these limitations, the following strategies can be considered:
Hybrid Architectures: Explore hybrid architectures that combine RAU-Net with other upsampling techniques like progressive growing GANs to preserve fine details during the generation process.
Adaptive Attention Mechanisms: Develop adaptive attention mechanisms in MSW-MSA that can dynamically adjust the window size based on the content complexity, allowing for better capture of long-range dependencies.
Regularization Techniques: Implement regularization techniques to prevent over-smoothing or loss of details during the upsampling process in RAU-Net, ensuring that important image features are retained.