以美学为导向的低光增强
概括
本研究介绍了以美学为导向的低光图像增强 (ALL-E),通过结合人类偏好来提高图像质量. ALL-E+模型增强和否定图像,在主观和客观评估中表现优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 低光图像增强 (LLE) 性能是主观的,需要人类偏好集成.
- 目前的LLE方法缺乏对美学品质的考虑,使用可能不足的启发式标准.
研究的目的:
- 引入一种新的范式,以美学为导向的低光图像增强 (ALL-E),结合人类的美学偏好.
- 开发一个强化学习框架,为训练LLE模型提供审美奖励.
- 介绍ALL-E+,这是一个统一的框架,用于连续增强和消除噪音.
主要方法:
- 提出了以美学为导向的低光图像增强 (ALL-E) 范式.
- 使用强化学习框架与美学奖励,其中像素作为代理人.
- 开发了ALL-E+,这是一个两阶段的增强和消毒方法.
主要成果:
- 在主观视觉体验和客观评估方面,ALL-E+显著改善.
- 整合美学偏好提高了处理图像的视觉质量.
- 提出的方法在各种基准上优于最先进的LLE技术.
结论:
- 以美学为导向的LLE对于提高主观视觉质量至关重要.
- ALL-E和ALL-E+框架提供了一个强大的方法来解决LLE和Denoising.
- 拟议的方法比现有的低光图像增强技术有了显著的进步.
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