Applications of Deep Learning-Based Image Generation and Processing in Visual Communication Design

With the continuous advancement of deep learning technology, its application in visual communication design has gradually become a research hotspot. This paper explores the application of deep learning-based image generation and processing in visual communication design, focusing on the specific applications of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) in tasks such as image generation, classification, segmentation, denoising, and enhancement. First, Generative Adversarial Networks (GANs) generate photorealistic images through adversarial training, driving advancements in style transfer, image synthesis, super-resolution reconstruction, and image restoration/inpainting. This provides designers with more creative and artistic design tools. Second, convolutional neural networks (CNNs) demonstrate formidable capabilities in image classification, segmentation, localization, and denoising/enhancement, enhancing the precision and efficiency of image processing. While deep learning exhibits significant advantages in visual communication design, its limitations-including strong data dependency, high computational resource requirements, and poor model interpretability-restrain its widespread adoption. © 2025 IEEE.