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<p>This study addresses the growing demand for image processing in visual communication design by proposing a deep learning (DL)-based algorithm to enhance creative efficiency and precision. The algorithm integrates DL technologies to optimize image processing workflows and applies them to design practice, significantly improving processing efficiency. Experimental results demonstrate that this algorithm performs excellently in processing efficiency and user experience compared to traditional methods. Particularly in preventing overfitting, the algorithm exhibits stronger stability and lower error rates, further validating the potential of DL applications in visual communication design and image processing. Additionally, the system adapts well to diverse image data types and design styles, demonstrating excellent scalability that provides robust support for personalized design and innovation. The main contributions of this study include the introduction of DL technology to optimize image processing in visual communication design, thereby improving image quality and artistic expression. An innovative convolutional neural network-based algorithm is proposed to achieve more precise image processing. Simultaneously, efficient model training strategies are designed to address challenges in image resolution, color optimization, and content generation, enhancing processing efficiency and intelligent capabilities. These research outcomes hold significant application value across visual communication design, advertising creativity, and multimedia art fields.</p>

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Image processing algorithm of visual communication design based on deep learning in digital background

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月26日

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  • Xugang Hou,
  • Qian Liu,
  • Xiaoying Zhang

摘要

This study addresses the growing demand for image processing in visual communication design by proposing a deep learning (DL)-based algorithm to enhance creative efficiency and precision. The algorithm integrates DL technologies to optimize image processing workflows and applies them to design practice, significantly improving processing efficiency. Experimental results demonstrate that this algorithm performs excellently in processing efficiency and user experience compared to traditional methods. Particularly in preventing overfitting, the algorithm exhibits stronger stability and lower error rates, further validating the potential of DL applications in visual communication design and image processing. Additionally, the system adapts well to diverse image data types and design styles, demonstrating excellent scalability that provides robust support for personalized design and innovation. The main contributions of this study include the introduction of DL technology to optimize image processing in visual communication design, thereby improving image quality and artistic expression. An innovative convolutional neural network-based algorithm is proposed to achieve more precise image processing. Simultaneously, efficient model training strategies are designed to address challenges in image resolution, color optimization, and content generation, enhancing processing efficiency and intelligent capabilities. These research outcomes hold significant application value across visual communication design, advertising creativity, and multimedia art fields.