![]() ![]() Your child learns through this iterative process, as neural networks do. You give some guidance along the way - you tell them when they're wrong - but eventually they learn how to sing do-re-mi-fa-so. They try, they fail, and you keep singing your 'ground truth'. You, in your infinite auricular wisdom, sing do-re-mi-fa-so back, but in tune. ![]() The idea is this: your child has some rudimentary understanding of, say, how to sing do-re-mi-fa-so. Think raising a child: With some structure and a whole lot of examples, you can train your child to do things he or she never imagined possible. At its simplest, ML is an attempt to train an algorithm to learn and do something it wasn't previously capable of. Here's where machine (ML) learning comes in. What if, instead, we could sharpen and upsample based on context? Machine learning Furthermore, simple 'brights brighter, darks darker' contrast boosts around high frequency content can only net you so much 'real', new information or detail increase. There's a limit to how discriminating such an approach can be: what is high enough frequency to be sharpened and what is low enough frequency to be left alone? Quite often, sharpening of detail leads to sharpening of noise. This is how unsharp mask creates the impression of increased sharpness. In the 'intensity profile' at bottom right, note the dip in darks and the boost in brights at the edge boundary. But simple sharpening algorithms are rather crude: the popular unsharp mask is really just a command that says 'make brights brighter and make darks darker', but only in high frequency portions of the image, leaving lower frequencies like areas of smooth skies alone. No one ever complained about more detail we photographers apply sharpening all the time, and sometimes even upsample our photos to higher resolution for the purposes of larger prints, higher resolution and bigger displays, to name a couple of use-cases. Does moving it to the right feel like an 'Enhance!' super-power? Does moving it to the left feels like smearing vaseline over your lens? If so, read on to find out how machine learning can perhaps make your images better - even images you've already shot.Īdobe's goal was simple: to increase the amount of detail in your photos. Let's try something: in the slider below, move the little circle with the arrows left and right. Back then, Sharad Mangalick and his team used machine learning to more accurately demosaic Bayer and X-Trans CFA Raws, to avoid common issues like moiré, false color and problematic rendering of edges. It builds on the 'Enhance Details' demosaicing algorithm developed by Adobe a couple years ago. Along with collaborators Michaël Gharbi and Richard Zhang of Adobe Research, Eric and the team produced a tool that extracts more detail from your photos without the typically concomitant noise penalty, by throwing machine learning at the problem. Photo: Rishi Sanyalīack in March, Senior Principal Scientist Eric 'I like to mess around with pixels' Chan published a blog post on outlining the strides he and his team had made at Adobe using machine learning to make your photos better. Super resolution of this Canon 5D Mark III image from 2013 yields an 88.5 MP file ripe for a super large canvas print. ![]()
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