Comments (7)
Thank you so much ^ ^
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@codeslake Thank you!
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Hi, @ryanxingql.
The predicted defocus map is in the range [0, 1], so the scipy.misc.toimage()
function does not normalize the defocus map as we provided the appropriate range. Note that the ground truth defocus maps are also in the same range between 0 and 1.
We've provided sigma_map
to get the actual sigma value.
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Thank you!
And also, what is the use of the sigma map? Is it for deblurring?
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Thank you! And also, what is the use of the sigma map? Is it for deblurring?
Yes!
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Hi @codeslake, I would like to ask you a question.
My understanding:
Our gray-scale defocus maps are ranging from 0 to 255.
Therefore, pixels with 0-values are focused, while the maximal available defocus value is 255 since the maximal COC for training is 15.
If a object in test images reaches out of this COC, its defocus value is still 255. In this situation, the output is somehow incorrect.
Am I right? Thank you.
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Yes, you are right.
DMENet will not be able to accurately predict the COC values of a test image, if the COC values are larger than the maximum COC used to create the training set.
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Related Issues (20)
- Output of DMENet HOT 6
- pertained model download link does not work HOT 1
- All-in-focus image generation HOT 9
- RTF dataset HOT 7
- cannot download the dataset and pretrained_model HOT 2
- Problem HOT 2
- OOM issue + pytorch version request HOT 3
- cannot download the dataset HOT 4
- Generate ground-truth binary blur masks HOT 2
- Defocused Image Generation HOT 1
- How to set the lambda in deconvolution HOT 1
- test dataset HOT 5
- Tensorflow v1 is no longer supported HOT 3
- About Pretrained weight
- About the data HOT 1
- How to apply deconvolution from masks? HOT 3
- Generate Defocus Maps HOT 1
- Ram and Memory issue HOT 1
- The max_coc of datasets is not 28. HOT 1
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