[1] - https://github.com/cchen156/Learning-to-See-in-the-Dark/blob...
If you want to know what the next hot thing in software engineering will be, just pay attention to whatever Jeff Dean is doing.
There's also the issue of how hard it is to select the line of code (API call) that does the job (because the API surface is huge), which is nearly invisible attribute.
Mathematica is famous for being incredibly powerful with low custom coding, but also very hard to find the API call that does the precise thing you need.
IMHO, credit should always go to Alex Krizhevsky for the rapid spread of deep learning. He has shown us it was possible. Even without Tensorflow and PyTorch, we will be fine with Caffe, torch, mxnet or Julia.
Generally, you just need to subtract the right black level and pack the data in the same way of Sony/Fuji data. If using rawpy, you need to read the black level instead of using 512 in the provided code. The data range may also differ if it is not 14 bits. You need to normalize it to [0,1] for the network input."
The Sony and Fuji training code looks mostly the same - they haven't bothered to pull out common code and re-use.
But, many DNN concepts (and ML concepts themselves) can be described with a few lines of pseudocode. CNNs, RNNs, etc. can all be described in a few lines.
It's really quite amazing, most of the work goes into first creating the net work from theory, then training and tuning it until you get good results.