Comments (4)
Actually, you are right. Since, a set of isotropic Gaussian kernels is a subset of anisotropic Gaussian kernels, so we mentioned in that manner.
Precisely, we used anisotropic Gaussian kernels for the blur kernels.
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Thanks for your comment. Indeed, isotropic Gaussian kernels is a subset of anisotropic Gaussian kernels.
May I ask more about the N (number of kernels used for meta training)? If my understanding is right, it's a pretty big number, as for every loop, some kernels will be generated. Is this true?
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Yes, you are correct. The number of kernels is theoretically infinite. The kernels are generated based on randomly sampled Gaussian parameters in every loop.
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Thank a lot for your comment.
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