通过使用深度外部上下文感知编码器来增强扩散网络,以实现低光图像增强
Pengliang Tang1, Yu Wang1, Aidong Men1
1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究介绍了ECA-Diff,这是一种用于低光图像增强 (LLIE) 的新型扩散模型. 它使用外部编码器高效地增强上下文,显著提高图像质量,而无需高计算成本.
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 (LLIE) 具有挑战性,因为复杂,广泛的退化.
- 现有的扩散模型与全球上下文建模作斗争,导致高计算成本.
研究的目的:
- 开发一种高效的基于扩散的LLIE方法,以增强远程上下文建模.
- 为了减少与全球模块集成在扩散骨干中相关的计算开销.
主要方法:
- 提出了ECA-Diff,这是一个带有外部上下文感知编码器 (ECAE) 的传播框架.
- 利用隐形空间上下文网络与混合变压器转换块进行特征提取.
- 为了培训规范化,实施了CIELAB空间发光度适应色差损失.
主要成果:
- 在配对和不配对的基准指标上,ECA-Diff的表现优于最先进的LLIE方法.
- 在全参考 (PSNR,SSIM,LPIPS) 和无参考 (NIQE,BRISQUE) 的指标上都取得了卓越的表现.
- 外部上下文路径只增加了适度的计算开销.
结论:
- 将全球上下文估计与代性解密脱,有效地促进了基于扩散的LLIE.
- 对于低级别的图像恢复任务,ECA-Diff提供了一个可通用的计算-一次调节范式.
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