I have an image of some data which is approximately 4,000 x 8,000 pixels. I am interested in finding out if anyone has used a deep learning algorithm to predict what would be on the image if it extended 100 more pixels in each direction. I would imagine that the data could be trained on smaller rectangles, and then the rules developed would be used to extend beyond the image given. Has anyone seen a problem like this (and is there a reference)? Even if not, what deep learning scheme would be best for this?
I think the closest problem that has been addressed with deep learning is image inpainting, that is, filling a blacked out region in the image:
For instance, this paper: Semantic Image Inpainting with Perceptual and Contextual Losses.
So it is certainly possible to fill missing information from an image with deep learning.
There are quite a few papers on predicting the next image in a set of video sequences. So I would familiarize yourself with those first.
With that being said it is definitely possible to do this sort of things using ML. There has been a lot of work on recurrent layers for Convolutional Neural Nets. These at a high level seems like it would be a good candidate to investigate for your initial architectures.
Here is some info on RCNNs:
Example RCNN in keras: link