Disclaimer 1: There were other questions related to the broad nature of our help-center in regard to what is on-topic, none of them have a good or clearly answer, in my opinion.
Data Science in its nature is a broad subject with applications ranging from NLP and Computer Vision to Predicting Stock Prices and Future Revenue, so it is quite difficult to pin-point what is and what is not on topic as basically anything can benefit from Data Science techniques.
Images are 2D Digital Signals, and Image Processing is clearly a subtopic of Signal Processing SE but with relevant influence on Computer Vision which is one of the most successful Deep Learning Application.
As basically anything can be related to DS and as the help center is pretty broad about what is on-topic on DS SE how should people define where to ask?
See for example this question from Dawny33♦, a high reputation and clearly an important member of our community:
- The question is: What does 'energy' in image processing mean?
It points to a Data Science related article Seam Carving for Content-Aware Image Resizing, but the question itself is purely about image processing, a subset of Digital Signal Processing.
As Cross Validated covers machine learning models them selves and Signal Processing covers things like digital image processing it would be on-topic to have pure machine learning or pure digital image processing question on Data Science Stack Exchange?
Or should we accept this kind of question once Data Scientist themselves are problem solvers and might have to tackle outside their field of expertise to solve Data Science problems without the need to contextualize the subject inside Data Science? (One can ask about DIP to understand it in order to later apply that to a DS system)