Artificial intelligence keeps getting better. A group of scientists from China has developed an algorithm that can turn sketches into realistic portraits. It even works with pretty rough sketches, and the end results look very close to real photos of people.
This AI turns pixelated faces into real portraits, but not without hiccups
We’ve already seen some AI software that can upsample low-res images. You know, CSI-style. Face Depixelizer is another AI-powered software, particularly focused on faces. It can take a pixelated, low-res photo and turn it into a realistic portrait. While the results are pretty impressive – the app doesn’t come without its quirks.
New Luminar 4 will use AI skin enhancer to automatically make your portraits flawless
Luminar 4 is only days from being released, and Skylum is gradually revealing the AI-powered features it’s going to offer. After automatic sky replacement and smart background enhancement, Skylum is now showing off the automatic Skin Enhancer & Portrait Enhancer. They’re made to assist your retouching process and make it faster than ever before.
Luminar 3 update kills it with object extrusion
Skylum has just announced its latest update to its Luminar photo editing software. The Luminar 3.1.0 brings new and improved Accent AI filter, which now features content-aware recognition. There are a few other improvements, so let’s see what the upgraded version of Luminar brings.
Adobe Lightroom and Camera Raw now use AI to enhance details in your photos
Adobe has been using its AI-powered Sensei technology to introduce various upgrades to its apps. In the most recent upgrade, Camera Raw, Lightroom Classic, and Lightroom CC became able to enhance details in your images and give you up to 30% higher resolution on raw files.
Ten Photoshop tips in under ten minutes to help you improve any photo
What I like about Photoshop is that there often several ways to get the result you want. Nathaniel Dodson of Tutvid shares some of his favorite techniques for performing different kinds of enhancements, from converting photos to black and white to cleaning up skin. In this video, you’ll see plenty of useful and quick techniques to add to your bag of tricks.
New neural network repairs damaged and low-quality images
We’ve seen some of the algorithms that can enhance low-quality photos. The researchers from Oxford University and the Skolkovo Institute of Science and Technology in Moscow have developed a new approach for restoring damaged or low-quality images. Instead of training the neural network with thousands of photos, their system called Deep Image Prior works everything out from the original image. And without any previous learning, it turns a pixelated or damaged photo into a hi-res one.
New algorithm “enhances” low-res photos till they are tack sharp in hi-res
The scientists of Max Planck Institute for Intelligent Systems in Germany have developed a new algorithm. It enhances low-res images so that they miraculously become hi-res and sharp. It only needs a single low-resolution input, and it will increase its resolution while retaining the realistic textures and details.
How to enhance colors in sunset photos with a single layer, and get optimal results
Even the magical light of the golden hour requires some enhancement in post-processing. There are a few ways to do it, and Denny Tang of Denny’s Tips suggest one of the simplest I’ve seen so far. He uses a single adjustment layer, and it’s the Channel Mixer. The whole editing process is pretty fast, yet gives natural-looking results on the photos taken during sunset (or sunrise).
Google RAISR’s upsampling brings CSI-like image enhancement to your Android device
RAISR stands for Rapid and Accurate Image Super-Resolution. It’s Google’s prototype software which utilises machine learning to provide better quality upsampling of low resolution images. They first showed off the technology in November last year, but now Google have announced that RAISR has been implemented into Google+ for Android.
The point of the technology is to save bandwidth. Many mobile users have fairly limited bandwidth. Either they have low limits, or it’s just slow. Google see RAISR as an option to save bandwidth. The idea is to scale down the images before sending out. This means they’re smaller and easier to send. Then RAISR blows them back up to their original size on the receiving end. And it wants to do this with the minimal of impacts on quality.
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