Poster Design Research Based on Deep Learning Automatic Image Generation Algorithm

Xiaoxi Fan and Yao Sun
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Xiaoxi Fan: College of Arts, Sichuan University, Chengdu, Sichuan 610207, P. R. China
Yao Sun: College of Arts, Sichuan University, Chengdu, Sichuan 610207, P. R. China

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 05, 1-21

Abstract: Although this research has made significant progress in image generation models, they still face issues such as insufficient diversity of generated images, poor quality of high-resolution images, and the need for a large amount of training data for model optimization. This paper studies poster design based on deep learning automatic image generation algorithm, using a recursive supervised image generation algorithm framework of generative adversarial networks for multi-view image generation and super-resolution generation tasks of small sample digital poster images. Various improvements have been proposed to enhance the performance of the GAN network model for poster design image generation tasks. Based on experimental research, this paper’s model uses generative adversarial networks to distinguish randomly cropped low resolution and high-resolution poster blocks, ensuring that high-resolution posters maintain their original resolution canvas texture and brush strokes, effectively improving the automatic generation effect of poster images. The evaluation results show that the quantitative evaluation of the proposed algorithm model in knowledge management is distributed in a reasonable range, which indicates that the proposed algorithm model has good performance in knowledge management. The poster design model based on deep learning automatic image generation algorithm proposed in this paper has certain effects. In subsequent practice, the automatic image generation algorithm can be combined with practical needs to improve the efficiency and design effect of poster design.

Keywords: Deep learning; image generation; poster design; generative adversarial networks; recurrent supervision (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219649224500692

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