暗噪声扩散:用于低光图像消解的噪声合成
IEEE transactions on pattern analysis and machine intelligence
|August 13, 2025
概括
使用扩散模型合成现实的低光噪声对于训练有效的消除噪声网络至关重要. 我们适应的扩散模型产生高精度的噪声,使最先进的低光图像增强成为可能.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 低光摄影的信号与噪声比率很低,这挑战了传统的无声化方法.
- 深度学习需要大量的配对数据集,这些数据集在低光条件下很难获得.
- 合成现实的低光噪声对于推进无声化技术至关重要.
研究的目的:
- 调查扩散模型在捕捉复杂的低光噪声分布方面的有效性.
- 开发一种适应的扩散模型,能够产生高精度的低光噪声.
- 允许创建大型合成数据集,用于训练低光消噪网络.
主要方法:
- 提出了一个双分支的扩散模型架构,以区分信号依赖和信号独立的噪声.
- 嵌入位置信息以准确建模固定模式噪声.
- 开发了针对低光噪声特征优化的定制扩散噪声时间表.
主要成果:
- 证明纯粹的扩散模型不足以实现现实的低光噪声合成.
- 通过拟议的调整实现了低光噪声分布的高精度生成.
- 产生了大型合成数据集,显著改善了低光照明网络性能.
结论:
- 适应的扩散模型为合成现实的低光噪声提供了可行的解决方案.
- 拟议的模型能够在低光条件下实现最先进的图像消噪性能.
- 进一步的分析为低光噪声的特征和产生提供了更深入的见解.
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