I am fairly new to machine learning. I am trying to generate a new image from other images of the same shape. An example of the image I'm trying to generate is an Hi-C data matrix: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT5_h4AXnOD3HReBIjZA8vBuv-DE-kedSVA4Q&usqp=CAU. I have many passes of an image and I am trying to recover the initial image from such passes (which would in turn have the same resolution).
I am aware of GANs but I could not find an example of them being used with multiple images as inputs instead of only one. I thought of using a normal network and feeding along with each pixel, the value of the neighboring ones but that would mean having thousands of variables as inputs. This is because, in a normal scenario I would have 14 images and, for each pixel I could have anywhere from (8 to 120) * 14 additional inputs (assuming a radius of 3 to 11).
And, at the same time I would like to include information about each neighbouring pixel as for the problem I am working on, a single data point is influenced by neighbouring ones.
Is what I am trying to achieve possible? Is there any examples of such network architecture being used somewhere?