结合Raw视频的DemoSicking前后的消除噪音
概括
本研究引入了相机传感器数据的自我相似性否定方法. 在demosaique之前和之后平衡过器可以提高图像质量,特别是在高噪音水平的情况下.
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:
- 在将原始摄像头数据转换为可显示的图像时,除是至关重要的.
- 深度学习已经在消除噪音方面取得了进展,但适应不同噪音水平和场景仍然存在挑战.
- 传统的denoising往往在demosaicking之前,尽管存在替代秩序.
研究的目的:
- 为拜耳模式的CFA视频数据提出基于自我相似性的无效化方案.
- 为了平衡demosaicking前后的denoising,以获得最佳的图像质量.
- 为了增强纹理重建,并适应各种噪音条件.
主要方法:
- 一个以自我相似性为基础的计划,重量化前和后的demosaickingdenoisers.
- 在每个denoiser之前整合时间轨迹预过.
- 在传感器上的噪声模型估计用于适应.
主要成果:
- 在demosaicking前和后的denoisers之间的平衡产生了优越的图像质量.
- 较高的噪音水平更受益于预先demosicking的消极影响.
- 时间预过可以提高纹理重建的准确性.
结论:
- 提出的方法准确地适应任何噪声水平,输入最小.
- 它实现了最先进的性能,使其适合于现实世界的录像.
- 该方法有效地解决了当前神经网络拒绝技术的局限性.
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