非线性噪声2噪声用于高效蒙特卡洛去噪器训练
Nonlinear Noise2Noise for Efficient Monte Carlo Denoiser Training
AI总结：
非线性噪声2噪声方法通过减少偏差和异常值影响，实现高效蒙特卡洛去噪器训练。
摘要：
噪声2噪声方法允许使用输入和目标图像对来训练基于机器学习的去噪器，其中输入和目标图像都可以是噪声的。这消除了需要使用干净的目标图像进行训练的需求，而干净的目标图像可能难以获取。然而，噪声2噪声训练有一个主要限制：对噪声目标应用非线性函数会扭曲结果。这种偏差发生的原因是非线性性使噪声目标的期望值与干净的目标图像不同。由于非线性函数在图像处理中很常见，避免它们会限制可以在噪声目标上进行的预处理类型。我们的主要见解是，某些非线性函数可以应用于噪声目标而不显著增加结果的偏差。我们开发了一个理论框架来分析这些非线性性的效果，并描述了一类具有最小偏差的非线性函数。我们展示了我们的方法在高动态范围（HDR）图像去噪中的应用，这些图像由蒙特卡洛渲染生成。噪声2噪声训练在HDR图像上会遇到困难，因为训练过程会被异常值淹没并表现不佳。我们考虑了一种常用的解决这些问题的方法：在模型输出和目标图像上应用非线性色调映射函数以降低动态范围。这种方法以前被认为与噪声2噪声训练不兼容，因为涉及非线性性。我们展示了某些损失函数和色调映射函数的组合可以减少异常值的影响，同时引入最小的偏差。我们应用我们的方法到一个现有的基于机器学习的蒙特卡洛去噪器上，其中原始实现是使用高样本计数参考图像训练的。我们的结果接近原始实现的水平，但仅使用噪声训练数据即可产生。
英文摘要：
The Noise2Noise method allows for training machine learning-based denoisers with pairs of input and target images where both the input and target can be noisy. This removes the need for training with clean target images, which can be difficult to obtain. However, Noise2Noise training has a major limitation: nonlinear functions applied to the noisy targets will skew the results. This bias occurs because the nonlinearity makes the expected value of the noisy targets different from the clean target image. Since nonlinear functions are common in image processing, avoiding them limits the types of preprocessing that can be performed on the noisy targets. Our main insight is that certain nonlinear functions can be applied to the noisy targets without adding significant bias to the results. We develop a theoretical framework for analyzing the effects of these nonlinearities, and describe a class of nonlinear functions with minimal bias. We demonstrate our method on the denoising of high dynamic range (HDR) images produced by Monte Carlo rendering. Noise2Noise training can have trouble with HDR images, where the training process is overwhelmed by outliers and performs poorly. We consider a commonly used method of addressing these training issues: applying a nonlinear tone mapping function to the model output and target images to reduce their dynamic range. This method was previously thought to be incompatible with Noise2Noise training because of the nonlinearities involved. We show that certain combinations of loss functions and tone mapping functions can reduce the effect of outliers while introducing minimal bias. We apply our method to an existing machine learning-based Monte Carlo denoiser, where the original implementation was trained with high-sample count reference images. Our results approach those of the original implementation, but are produced using only noisy training data.
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