交流变压器:用于任意图像曝光校正的统一变压器
Chao Li1, Yuchen Yang1, Qiujia He1
1School of Statistics and Data Science, Nankai University, Tianjin, 300071, China.
概括
这项研究将单图像曝光校正 (SEC) 和多图像曝光融合 (MEF) 统一为任意曝光校正 (AF-EC). 建议的ACformer模型有效地处理任何数量的输入,提高SEC和MEF任务的性能.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 单图像曝光校正 (SEC) 和多图像曝光融合 (MEF) 解决了图像的暴露不足/过度暴露.
- 现有的方法专门用于SEC或MEF,忽视了它们的基础任务相关性.
- 需要采用统一的方法来利用SEC和MEF之间的共同点.
研究的目的:
- 将单图像曝光校正 (SEC) 和多图像曝光融合 (MEF) 统一为一个单一的"任意曝光校正" (AF-EC) 任务.
- 开发一个集成的深度学习模型,能够处理任意数量的输入以进行曝光校正.
- 通过使用统一框架,在SEC和MEF任务上展示相互提升的性能.
主要方法:
- 为统一的AF-EC任务提出了一个任意曝光校正变压器 (ACformer) 模型.
- 在ACformer中引入了一个并行特征融合和校正 (PFFC) 模块.
- 采用空间,通道和时间自我注意机制,用于在多个中进行特征级别的校正和融合.
主要成果:
- 与专门的SEC和MEF方法相比,ACformer模型实现了更高的性能.
- 实验表明,客观指标和主观视觉质量都有显著的改善.
- 统一的方法显示了在处理各种曝光纠正场景时的增强能力.
结论:
- 拟议的ACformer有效地统一了AF-EC框架下的SEC和MEF任务.
- 综合模型展示了整体地解决暴露纠正的好处.
- 未来的工作可以探索任意图像处理的进一步进展.
相关概念视频
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