Researchers have designed an AI-powered tool that transforms a blurry portrait into the perfect selfie. The method, called PULSE, searches through AI-generated examples of high-resolution faces to match ones that look similar to the input image when compressed to the same size. The system is capable of converting a 16x16-pixel image of a face to 1024 x 1024 pixels in a few seconds, which is 64 times the resolution. The method, called PULSE, searches through AI-generated examples of high-resolution faces to match ones that look similar to the input image when compressed to the same size. Duke University computer scientist Cynthia Rudin, who led the team, said: 'Never have super-resolution images been created at this resolution before with this much detail.'
Google's neural networks have achieved the dream of CSI viewers everywhere: the company has revealed a new AI system capable of "enhancing" an eight-pixel square image, increasing the resolution 16-fold and effectively restoring lost data. The neural network could be used to increase the resolution of blurred or pixelated faces, in a way previously thought impossible; a similar system was demonstrated for enhancing images of bedrooms, again creating a 32x32 pixel image from an 8x8 one. Google's researchers describe the neural network as "hallucinating" the extra information. The system was trained by being shown innumerable images of faces, so that it learns typical facial features. A second portion of the system, meanwhile, focuses on comparing 8x8 pixel images with all the possible 32x32 pixel images they could be shrunken versions of.
It's no secret that we're pretty big fans of machine learning and we love thinking of new and exciting ways to use it in Pixelmator Pro. Our latest ML-powered feature is called ML Super Resolution, released in today's update, and it makes it possible to increase the resolution of images while keeping them stunningly sharp and detailed. Yes, zooming and enhancing images like they do in all those cheesy police dramas is now a reality! Before we get into the nitty-gritty technical stuff, let's get right to the point and take a look at some examples of what ML Super Resolution can do. Until now, if you had opened up the Image menu and chosen Image Size, you would've found three image scaling algorithms -- Bilinear, Lanczos (lan-tsosh, for anyone curious), and Nearest Neighbor, so we'll compare our new algorithm to those three.
Over the past few years, film and video standards have continued to evolve. There is a growing demand for higher fidelity imagery and resolutions to deliver a more immersive viewing experience. With 4K as the current standard and 8K experiences becoming the new norm, older content doesn't meet today's visual standard. The remastering process aims to revitalize older content to match these new standards. It has become a common practice in the industry, allowing audiences to revisit older favorites and enjoy them in a modern viewing experience.
Various image effects have been receiving increasing attention in recent years. A popular example is bokeh, a blur on an out-of-focus region in a photograph. This effect is achieved by using a fast camera lens with a wide aperture. Unfortunately, it is almost impossible to reproduce this kind of effect with mobile phone cameras because they do not meet the necessary technical specifications. However, if each image pixel is classified into person and background categories, the bokeh effect can be simulated by blurring just the background.