These combine style transfer with some manual editing. I'm using the deep learning output as a tool to get images I like to see, and I like to see what I can get it to do. Some of the tricks I've learned:
- If you want to add details to a real item, make sure that the scale and lighting are consistent between the source and style images.
- Monochrome is generally more successful than depending on it to get color right.
- Bas-relief style details, and other shallow sculptural details work really well.
- If two images are very similar, you can use the details of the high-resolution one to enhance the low resolution one. But this requires a very close correspondence.
- You can make fractals by doing the low-res to high-res trick repeatedly with the same image, zooming in on small parts of it.
- You can force the output to have symmetry by making sure both the source and style image are symmetric. This works especially well when the style image is symmetric with some shallow depth but lit from one side. Then the output will be, too.
- You can use frequency decomposition to add details to an image without affecting the overall composition. In Photoshop, this is done by the following process:
- detail image: high pass at radius n, linear light blending mode, 50 % opacity, on top of:
- background image: gaussian blur at radius n