通过细粒度噪声估计进行无校准原始图像去除
概括
这项研究引入了一个新的管道,用于实现现实的图像噪声合成和估计. 该方法准确地模拟了相机特定的噪音,即使对于未知的传感器和低光条件,也提高了消除噪音的性能.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 深度学习已经推进了图像消除噪声,但实际应用需要更好的噪声建模和估计.
- 目前的方法侧重于现实的噪声模型,用于训练数据或噪声测量器的噪声水平估计.
研究的目的:
- 开发一种用于噪声估计和现实的噪声合成的创新管道.
- 将噪声建模和噪声估计策略连接起来,以改善噪声消除.
主要方法:
- 整合一个细粒度的统计噪声模型和对比学习.
- 使用独特的数据增强技术来增强学习.
- 开发一个没有校准的管道来估计噪声参数和合成现实的噪声.
主要成果:
- 管道有效地估计了未知摄像头的噪声参数,没有校准或配对数据.
- 在正常和低光场景中都证明了有效性.
- 成功合成相机特定的噪声分布,以获得现实的训练数据.
结论:
- 拟议的管道为现实世界噪音合成和消除提供了可适应和有效的解决方案.
- 估计细粒度噪声模型的参数可以在具有挑战性的低光条件下提高应用性.
- 无校准方法简化了各种成像传感器的噪声建模.
相关概念视频
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The LOD indicates the presence or absence...
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Analytical Balance Calibration
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Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...
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Deconvolution
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Downsampling
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The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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