Image retouching aims to enhance visual quality while aligning with users' personalized aesthetic preferences. To address the challenge of balancing controllability and subjectivity, we propose a unified diffusion-based image retouching framework called PerTouch. Our method supports semantic-level image retouching while maintaining global aesthetics. Using parameter maps containing attribute values in specific semantic regions as input, PerTouch constructs an explicit parameter-to-image mapping for fine-grained image retouching. To improve semantic boundary perception, we introduce semantic replacement and parameter perturbation mechanisms in the training process. To connect natural language instructions with visual control, we develop a VLM-driven agent that can handle both strong and weak user instructions. Equipped with mechanisms of feedback-driven rethinking and scene-aware memory, PerTouch better aligns with user intent and captures long-term preferences. Extensive experiments demonstrate each component's effectiveness and the superior performance of PerTouch in personalized image retouching. Code is available at: https://github.com/Auroral703/PerTouch.

翻译：图像润饰旨在提升视觉质量，同时贴合用户的个性化审美偏好。为应对可控性与主观性之间的平衡挑战，本文提出了一种统一的基于扩散模型的图像润饰框架，命名为PerTouch。该方法支持语义层面的图像润饰，同时保持全局美学一致性。PerTouch以包含特定语义区域属性值的参数图作为输入，构建了显式的参数到图像的映射，实现细粒度图像润饰。为增强语义边界感知能力，我们在训练过程中引入了语义替换与参数扰动机制。为连接自然语言指令与视觉控制，我们开发了一种基于视觉语言模型（VLM）驱动的智能体，能够同时处理强约束与弱约束的用户指令。通过配备反馈驱动的反思机制与场景感知记忆模块，PerTouch能更好地对齐用户意图并捕捉长期偏好。大量实验验证了各模块的有效性，并证明了PerTouch在个性化图像润饰任务中的优越性能。代码发布于：https://github.com/Auroral703/PerTouch。