Comments (4)
The noise level depends on the settings in step 4) Accumulate
, to a large extend especially on the kDetail
and kDenoise
values which are the sigmas for areas detected as being not noisy or noisy (sigma = number of pixels averaged surrounding a given pixel). With a large value for kDetail
and kDenoise
you will get smoother images with less noise.
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Sorry, I don't understand. What do you mean by "color points"?
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Thank you for your response. I've run through the engineering, and the images show color noise. The enlarged image is shown in fig. You can see the color noise, but the original image is normal。
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Thank you for your explanation. When I read the image into the interface, I found that there was noise in the interface display without later processing.In addition, can we give some raw graphs like this.
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Related Issues (20)
- tuning parameter k2 HOT 2
- 5 merged "New York scene" looks worse than output from Adobe Camera RAW HOT 3
- The system cannot find the file specified HOT 2
- Could you please help to check this exception? HOT 1
- Does this approach need the camera parameters? HOT 4
- Is it possible to run the code in Ubuntu? HOT 2
- The super resolution can't resize the image with a two-fold resolution HOT 5
- ManagedCuda.NPP.NPPException:“Device allocation error” HOT 3
- System.DllNotFoundException:Unable to load DLL“nppisu64_10”: HOT 2
- How to create synthetic image bursts to run Google's Handheld Multi-Frame Super-Resolution algorithm? HOT 2
- about max and min in RobustnessModell.cu HOT 1
- Do quality analysis mentioned in Google's paper with TIFF and PNG photos HOT 2
- How does the super resolution works? HOT 1
- Possibility of using .tiff images? HOT 1
- The program is crashing when opening .dng files HOT 4
- Error loading DNG files from Fuji HOT 2
- Version Update
- Loaded images do not display in GUI HOT 1
- ManagedCuda exception HOT 1
- How do you open the program HOT 5
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