Deep Bilateral Learning for Real-Time Image Enhancement

Re:Deep Bilateral Learning for Real-Time Image Enhancement by vanderZwan on Wednesday August 02, 2017 @01:50PM (#14912925)

I really would like to see them try different learning sets that vary the "styles" of retouching. This example looks like it's strongly biased to the "make the images pop!" style of retouching, blowing highlights, shadows and contrasts.

What if the input set has more subtle retouching that pulls highlights and pushes shadows, but without the aforementioned issues?

What if they got their hands on the unedited and edited magnum photos? That would produce an interesting B&W filter, for sure!

https://www.slrlounge.com/magnum-photos-darkroom-magic-genes...

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Re:Deep Bilateral Learning for Real-Time Image Enhancement by londons_explore on Wednesday August 02, 2017 @06:43PM (#14915350)

I wonder how many images are required to train a network like this?

If it's in the millions, getting pre and post retouching image pairs in such a quantity is likely impractical.

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Re:Deep Bilateral Learning for Real-Time Image Enhancement by vanderZwan on Thursday August 03, 2017 @03:42AM (#14917703)

Ah, right, that could also be a limitation.

Although I'm pretty sure Magnum Photos has a large quantity of images, but perhaps not all in a consistent style.

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