Markov Chains can be used to generate, or auto-complete, text.
Training text is read, and some information about the text is remembered, for example:
The word "I" is usually followed by the word "have". The word "to" is usually followed by the word "go", and somtimes the word "be".
This can be used to generate in text like:
A pride of related females and a key species sought for exhibition in the four big cats in breeding programs for ten years, as injuries sustained from continual fighting with rival males exceeding 250 kg (550 lb) in historic times. Until the Lascaux and medieval cultures where they may take to Peru.
Although, this is a lot less effective with many other types of texts.
Q: Can example images be shown, where an aproach similar to the use of Markov Chains to generate/auto-complete text, has been used to process images?
Alternatively, is this still unrealistically computationally expensive?
I was unable to find examples by searching, I may be lacking the correct vocabulary to do so.
Using Markov Chains like this could be very computationally expensive, a small 5x5 neighborhood, in monocrome image (black and white only, no shades of grey) has 2^(5^2) ≈ 33 million possible combinations.
Such processing could include: Inpainting, synthisis, improving resolution and and error correction/checking.
Examples of inpainting and synthisis, using other approaches:
Inpainting: Gaps in images being fixed.
https://reference.wolfram.com/language/ref/Inpaint.html (click "open all").
Synthisis: For example Google Deep Dream, using convolutional neural networks and inceptionism.