I think that we just described the same process.

>-Sample random noise.

>-Ask ... ...what the network said.

>-Repeat until there is no noise left.

Initially we have noise (random or not doesn't matter) and we are trying to find inside that noise a matching function to an image that we already have, our model or training image. That image may or may not also contain some residual noise and as you describe in your iterative steps, noise is added to it thus decreasing the signal to noise ratio, and the neural network compares the output of it's initial or updated image to the "correct answer" image and weights the next iteration accordingly so that a better match can be obtained. The function itself consists of an optimization process designed to minimize the residual noise (a de-noiser like you say) between the most recent image and our target image.

When you say that you begin with a sample of random noise created by your array populating function which is assumed to be random but that you have no training image to which you are adding noise, that fits the whole process but it ignores that you are using the random noise image to iteratively produce an output that fits a model image created by compiling statistics from multiple images to create a target output that is assumed to be near noiseless or to have very high signal to noise ratio.

If you are trying to use this to "find" Alfred E. Neuman in your output the process needs to know what Alfred E. Neuman looks like so it can effectively optimize to that result. Each iteration denoises based on the known output in the model created from the images that it must ingest in order to build the model. If you only have a few images of Alfred E. Neuman but you have thousands of Homer Simpson in your model input dataset, then you will have to fight through the tendency of the process to converge on Homer Simpson. No matter, you always have a priori information that is used to verify the integrity of the output. The input is irrelevant whether it is random or not since you are looking at an optimization process that matches, denoises, and weights iteratively until it minimizes an error function in the match and can be said to be an optimum, or good match.

This is not particularly new or novel or anything else. It is a typical iterative modeling exercise like those that have been used for decades but now you have the compute power to build a near noise-free target model that fits the known data from every source at your disposal.

The user who created the Hollie Mengert styled outputs could not have hit that target without using a model that was designed to create or mimic that type of output. That is why he chose to use her work in his process. He liked it. Then when he found out that she was not pleased about not being consulted and that she didn't have the rights to use some of those images then I think he had a come-to-Jesus moment that ultimately led him to rename it so he could feel better about it. Guilt-tripped him.

Anyway, ethics should be a required part of every computer science curriculum especially when private personal information is involved.

I'm in the oil and gas industry. It sucks sometimes. Fortunately there has been a push to include or require ethics training. Maybe one day it will clean up that industry. I'm only holding my breath though when I pass a refinery.