Research on the Application of Machine Learning in Visual Image Design

With the rapid development of artificial intelligence, machine learning applications in visual design have become increasingly widespread, particularly in the field of image processing. This study presents an intelligent image generation platform based on Generative Adversarial Networks (GANs) aimed at improving design efficiency and creative expression. The platform integrates Deep Convolutional GANs (DCGANs) with style transfer techniques to efficiently generate high-quality images with artistic and visual appeal. Experimental results demonstrate that the model outperforms traditional methods in terms of clarity (PSNR of 28.4 dB), realism (Inception Score of 7.5), and style consistency (Style Loss of 0.0025). User experience evaluations indicate that designers rate the platform highly for ease of use and image generation quality. This model not only enhances design efficiency but also serves as a powerful tool for creative visual tasks, advancing the automation and intelligence of the design process.