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Just one more thing! Please confirm your subscription to Verge Deals via the verification email we just sent you. Email required. By signing up, you agree to our Privacy Notice and European users agree to the data transfer policy. I'm an avid photographer and fairly tech-savvy, but Photoshop sometimes frustrates me more than helps.
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Luminar AI also contains portrait enhancement tools that autonomously identify human features while its AI Structure feature adds definition only to those areas of a picture where it's appropriate.
DxO PhotoLab 4 lets photographers make selective adjustments to Raw format files so detail can be restored. As with all machine learning, the dataset is king. The use of augmented reality in photography is controversial. Adobe Photoshop now contains a feature called Face Aware Liquify, which reveals and corrects unwanted distortions in an image.
So does the increase of use of AI in photography and videography mean you should be more suspicious of photos and videos? Yes, but the flip side is that AI is making slick-looking photography accessible to all. The world is not necessarily ready for the full implications of AI cameras. Google used AI on its Google Clips wearable camera, which used AI to capture and keep only particularly memorable moments.
For example, it auto-deleted photographs with a finger in the frame and out-of-focus images, and favored those that comply with the general rule-of-thirds concept of how to frame a photo. Creepy and controlling? Some thought so. In any event, Google pulled the camera in The question is not whether AI is powerful enough to do the things we want, but whether we're quite ready yet to hand so much power over to a machine Google Research and the University of California Berkeley published a paper about their new AI technique that can remove unwanted shadows from snapshots in less than ideal lighting conditions.
It works by applying machine learning algorithms to noisy video frames. Expect composition to change, too, as a direct result of ever-increasing resolution. Traditional approaches take a low-resolution image and 'guess' what extra pixels are needed by trying to get them to match, on average, with corresponding pixels in high-resolution images the computer has seen before.
As a result of this averaging, textured areas in hair and skin that might not line up perfectly from one pixel to the next end up looking fuzzy and indistinct. The Duke team came up with a different approach. Instead of taking a low-resolution image and slowly adding new detail, the system scours AI-generated examples of high-resolution faces, searching for ones that look as much as possible like the input image when shrunk down to the same size.
The team used a tool in machine learning called a "generative adversarial network," or GAN, which are two neural networks trained on the same data set of photos. One network comes up with AI-created human faces that mimic the ones it was trained on, while the other takes this output and decides if it is convincing enough to be mistaken for the real thing. The first network gets better and better with experience, until the second network can't tell the difference. PULSE can create realistic-looking images from noisy, poor-quality input that other methods can't, Rudin said.
From a single blurred image of a face it can spit out any number of uncannily lifelike possibilities, each of which looks subtly like a different person.
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