I am trying to use N2V to de-noise a series of fluorescent images. So far, it is all working quite nicely, but I was unsure about some aspects relating to the selection of training images. I had a look at the published papers and other documents I could find, but couldn’t really find any answers to my questions. So, I was wondering whether anybody had any advice on the following points:
Does it make any real difference whether the training is performed on a single or multiple images from the image set? Would it be better to extract training patches from more than one image?
Is it better to use two independent images for the extraction of training and validation patches or train and validate using patches extracted from the same image? Would it make any substantial difference?
If the labeled structures of interest on the image are relatively sparse (see sample below), is it better to train using cropped versions of the image that reduce the amount of ‘empty’ space (not really empty as it still contains background information, but just no structures of interest) or is it better to use the full-sized, original images?
Sorry if these are rather obvious questions, but I would be really grateful for some further insights beyond me just trying all the possible permutations. Are there general principles that I should be aware of, but that I have overlooked?
Thanks for your help,