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Hi, 首先非常感谢您复现并共享这个工程,为我理解论文和模型结构提供非常大的帮助。 其次有个问题需要请教,我看您训练及推理时的coeff_a/coeff_b是用RViDeNet标定的shot/read值直接除以(MAX-BL)进行归一化,可我看RViDeNet论文里噪声参数的标定过程是在12bit图像上进行的,请问可以直接这么用么?因为按照噪声模型y=kx+b的话推导不出12bit->归一化浮点数之间的线性关系呢? 烦请回复 BR
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您好!我认为这边是把每一个像素的噪声分布近似为一个高斯分布,那么归一化的过程中,令max=MAX-BL,那么归一化之后均值x'=x/max,方差sigma'=sigma/max^2,所以归一化之后的像素噪声满足方差sigma'=sigma/max^2=(kx+b)/max^2=(kx'*max+b)/max^2=kx'/max+b/max^2.
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您好,根据方差的性质,D(Ax)=A^2*D(x). 令sigma^2=ax+b=D(x), 则D(Ax)=A^2*D(x)=A^2*(ax+b)=(A*a)*(Ax)+(A^2*b).
D(Ax)=A^2*D(x)
sigma^2=ax+b=D(x)
D(Ax)=A^2*D(x)=A^2*(ax+b)=(A*a)*(Ax)+(A^2*b)
因此,归一化后的Ax,对应的coeff_a = A*a, coeff_b = A^2*b.
coeff_a = A*a, coeff_b = A^2*b
而实际上,我认为网络训练过程中,coeff_a/coeff_b是否进行归一化对结果的影响不是特别大,网络具备对这样线性权重的适应能力。
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Hi, 首先非常感谢您复现并共享这个工程,为我理解论文和模型结构提供非常大的帮助。
其次有个问题需要请教,我看您训练及推理时的coeff_a/coeff_b是用RViDeNet标定的shot/read值直接除以(MAX-BL)进行归一化,可我看RViDeNet论文里噪声参数的标定过程是在12bit图像上进行的,请问可以直接这么用么?因为按照噪声模型y=kx+b的话推导不出12bit->归一化浮点数之间的线性关系呢?
烦请回复
BR
The text was updated successfully, but these errors were encountered: