基于强化学习的顺序参数调用于图像信号处理
IEEE transactions on pattern analysis and machine intelligence
|December 11, 2025
概括
我们引入了用于优化图像信号处理 (ISP) 参数的新增强化学习模型,解决了手动调和黑子深度学习的局限性. 我们的方法提高图像质量和效率,即使数据有限.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 图像处理 图像处理
背景情况:
- 硬件图像信号处理 (ISP) 涉及复杂的参数调整,传统上是手动和主观的.
- 现有的深度学习方法经常忽视ISP模块之间的内在关系,将过程视为黑子.
研究的目的:
- 开发一个自动化和高效的ISP参数优化模型.
- 探索顺序ISP模块结构和参数合对调节的影响.
主要方法:
- 引入了一个单代理强化学习 (RL) 模型 (SARL-ISP) 进行连续的ISP参数优化.
- 提出了一个多代理RL (MARL-ISP) 框架,其中包括一个串行参数调模块 (SPTM) 和特征选择模块 (FSM).
主要成果:
- SARL-ISP和MARL-ISP模型在各种任务 (如对象检测和实例细分) 中展示了有效性和效率.
- 与最先进的方法相比,模型可以实现更高的性能,即使训练数据最小.
结论:
- 强化学习为优化硬件ISP参数提供了一个强大的框架.
- 拟议的SARL-ISP和MARL-ISP模型在图像质量和处理效率上提供了显著的改进.
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