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The input images were preprocessed similarly to the training set.
Note that the input images to depth_map are still.bmp files.
The input images must have the same sizes and are combined using logical coordinates.
Notably, our STORK framework is fully automated and does not require any manual augmentations or preprocessing on the input images.
These random phase masks are especially designed using the input images.
The recommendations, however, depend on the quality of the input images and the edge detection parameters.
The input images are considered as wide sense bivariate random processes.
The input images were too large to process using our DCNN due to the limitations in the size of GPU memory.
To cope with illumination and pose variations, 3D information is used for the normalization of the input images.
The key to this technique lies in interpreting the input images as 2D slices of a 4D function - the light field.
The assumption behind this approach is that the input images used for the calculation of the average image are uniform and indiscriminate in terms of the position of objects in the image.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com