基于强化学习的图像颜色调整优化方案设计与实现
Reinforcement Learning Environment Construction and Action Space Design for Image Color Adjustment Optimization
于战锋 1张海旸1
作者信息
- 1. 北京邮电大学计算机学院，北京100876
- 折叠
摘要
图像颜色调整优化是计算摄影与视觉信息处理领域的关键问题,旨在通过色调映射与参数微调提升影像的视觉表现力。现有基于深度学习的端到端图像增强方法虽然在像素级重构上表现优异,但缺乏对色彩调整过程的显式控制,难以实现符合特定视觉意图的精细化优化。强化学习通过序列决策机制为解决该问题提供了动态优化路径,但其性能高度依赖于交互环境的物理真实性与动作空间的搜索效率。针对当前图像颜色调整任务中环境交互机制缺失及参数搜索收敛困难的问题,本文提出一种面向图像颜色调整优化的强化学习环境构建与动作空间设计方案。研究首先将颜色调整过程建模为部分可观测马尔可夫决策过程,构建了融合色彩统计与语义特征的混合状态空间以表征图像色彩状态。在此基础上,针对高维连续色彩参数空间的优化难题,建立了一套基于参数化滤镜的离散动作空间,并推导了曝光、对比度及色温等核心维度的参数搜索策略。实验结果表明,该环境有效支撑了智能体的颜色调整策略学习,相较于传统方法,优化后的动作空间在收敛速度与图像颜色调整质量上均取得了显著提升。
Abstract
Image color adjustment optimization is a critical issue in computational photography and visual information processing, aiming to enhance visual expressiveness through tone mapping and parameter fine-tuning. Existing deep learning-based end-to-end image enhancement methods excel in pixel-level reconstruction but lack explicit control over the color adjustment process, making it difficult to achieve refined optimization aligned with specific visual intents. Reinforcement learning offers a dynamic optimization path for this problem through sequential decision mechanisms, yet its performance heavily relies on the physical fidelity of the interaction environment and the search efficiency of the action space. Addressing the lack of interaction mechanisms and the difficulty of parameter convergence in current image color adjustment tasks, this paper proposes a reinforcement learning environment construction and action space design scheme oriented towards image color adjustment optimization. The research first models the color adjustment process as a Partially Observable Markov Decision Process (POMDP) and constructs a hybrid state space fusing color statistics and semantic features to characterize image color states. On this basis, aiming at the optimization challenges of high-dimensional continuous color parameter spaces, a discrete action space based on parametric filters is established, and parameter search strategies for core dimensions such as exposure, contrast, and color temperature are derived. Experimental results indicate that this environment effectively supports the agent's strategy learning for color adjustment. Compared with traditional methods, the optimized action space achieves significant improvements in both convergence speed and image color adjustment quality.关键词
图像颜色调整/强化学习/环境构建/动作空间优化Key words
Image Color Adjustment/Reinforcement Learning/Environment Construction/Action Space Optimization引用本文复制引用
于战锋,张海旸.基于强化学习的图像颜色调整优化方案设计与实现[EB/OL].(2026-02-03)[2026-02-05].http://www.paper.edu.cn/releasepaper/content/202602-16.学科分类
计算技术、计算机技术
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