AIGC-Photoshop Collaborative Image Processing Method: AI-Assisted Design Optimized for Text Rendering

DOI

10.2991/978-94-6239-737-8_33How to use a DOI?

Keywords

Image Processing; AIGC; Photoshop; Text Rendering; Human-AI Collaboration; AI-Assisted Design

Abstract

This study proposes an AI-driven collaborative image processing method to address the lack of efficient synergy between AIGC and Photoshop, specifically mitigating semantic ambiguity, stroke fragmentation, and style inconsistency in text region generation. First, visual-textual dual features are extracted to build an editing behavior prediction model. Second, mapping rules from AIGC regions to PS editable layers are established, and a Deep Q-Network combined with a Conditional Diffusion Model generates optimized text restoration solutions. Finally, a human-AI workflow of “AIGC generation + PS refinement + designer tuning” is formed. Comparative experiments against pure PS manual and pure AIGC approaches evaluate text clarity, editing efficiency, and user satisfaction. Results show that the proposed method significantly outperforms both control groups in text readability, editing flexibility, and subjective ratings, validating its effectiveness in overcoming AIGC’s text generation weakness. Furthermore, we discuss the method’s limitations in extreme scenarios such as multilingual mixed text, handwritten fonts, and complex artistic fonts, including potential failure cases, thereby providing a more rigorous conclusion.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Yingfei Jia
AU  - Yang Liu
AU  - Lin Zhang
AU  - Weiqian Mao
PY  - 2026
DA  - 2026/08/18
TI  - AIGC-Photoshop Collaborative Image Processing Method: AI-Assisted Design Optimized for Text Rendering
BT  - Proceedings of the 2026 5th International Conference on Art Design and Digital Technology (ADDT 2026)
PB  - Atlantis Press
SP  - 263
EP  - 268
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-737-8_33
DO  - 10.2991/978-94-6239-737-8_33
ID  - Jia2026
ER  -