- https://github.com/Benjamin-Loison
- Joined on
2022-10-16 -
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Thought about using benchmark_raw_images_loading.py:
#!/usr/bin/env python
from tqdm import tqdm
IMAGES_CAMERAS_FOLDER = {
'RAISE': 'flat-field/nef',
'Rafael…class iterativeMean:
mean = None
numberOfElementsInMean = 0
def add(self, element):
if self.mean is None:
self.mean = element
else:
…It does not seem that can only load part of a raw image with rawpy, if even possible at all.
In getRawColorChannel:
rawImageVisible = raw.raw_image_visible.copy()[:100,…50 images for both cameras take about 22 GB of memory.
Should implement an enum to choose between RMS and correlation.
Implementation
So now let us correct the implementation to implement:
- $prnu_{camera}^l = \text{mean}(image_{camera}^{training_j} − denoiser(image_{camera}^{training_j}, l, camera) \text{…
I doubt that current iterative mean leverages the best its current knowledge of the training set.
More precisely we add to the estimated PRNU mean the image substracted the average of already…
Unclear if correlation should in absolute be close to 1 or just close to 1.
Is it the most appropriate, while staying in the use-case context, to compute {min,max}Color only on training images. Do not forget all denoisers.
Could compute proportion of pixels for mean outside largest other denoisers band.
But it may be a significant proportion exceeding not much, so should maybe just plot distribution of pixel…
Disk Image Writer?
Related to Benjamin_Loison/gimp/issues/29.
Could locate such maximums to potentially see visually other outliers.
>>> index = -100; np.partition(npArray.flatten(), index)[index]
27.28
>>> index = 100; np.partition(npArray.flatten(), index)[index]
-26.35