Depth-wise Separable GAN: Efficient and Scalable Generative Adversarial Networks

In computer vision, image denoising is a crucial task. The aim is to improve the visual quality of the image by eliminating unnecessary noise. Traditional approaches are often weak when it comes to dealing with complex corruption patterns. This study presents a novel approach grounded in generative adversarial networks. We use depthwise separable convolutions to enhance the image. It can reduce the number of arguments and computational costs. It can make our model more efficient while maintaining its performance. In our experiment, the image stabilizes at 256 x 256. The training utilizes Binary Cross-Entropy loss and the Adam optimizer for both the generator and discriminator. The results of experiments worked on human face datasets show that our model significantly improves the quality of repair. This approach provides a good framework for photo editing, object deletion, and image recovery.