I'm starting to study deep learning (i.e., convolutional neural networks and [Stacked] denoising autoencoders). I'm really stuck in how my neural network should be for this case:
I have some medical images (around 25 images, 100x100, grayscale from 0 to 255, unfortunately I can't show them here) with noise (Gaussian, as I was told) and I need to re-generate the same images removing the noise. I thought I could do it just using a denoising autoencoder (adapting the code from this example), but it looks like it just "adapts" from the image training set. For example, if I train my autoencoder with images of heads and knees, it can't reconstruct a shoulder image. (Actually, even a head or knee image is reconstructed wrongly.) What exactly should I do? In this paper (pdf), I understand that if I train a DA to remove the noise of a given set of data, it would remove noise from any other image.
EDIT: I have already tried my auto encoder with sigmoid activation, with 10, 30, 50, 100, 500, 1000, 5000 and 10000 neurons in the hidden layer and a variety of training epochs (from 50 to 100000) and it's not even close to a accept result.