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.
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.
What if you could use Artificial Intelligence to enhance your photos like those seen on TV? Image super-resolution is the technology which allows you to increase the resolution of your images using deep learning so as to zoom into your images. Image super-resolution is a software technique which will let us enhance the image spatial resolution with the existing hardware. Low Resolution(LR) Image: Pixel density within an image is small, hence it offers few details. High Resolution(HR) Image: Pixel density within an image is large, hence it offers a lot of details. A technique which is used to reconstruct a high-resolution image from one or many low-resolution images by restoring the high-frequency details is called as "Super-Resolution".
Taking Instagram-worthy photos is one thing, editing them is another. Most of us just upload a pic, tap a filter, tweak the saturation, and post. If you want to make a photo look good without the instant gratification of the Reyes filter, enlist a professional. Researchers from MIT and Google recently showed off a machine learning algorithm capable of automatically retouching photos just like a professional photographer. Snap a photo and the neural network identifies exactly how to make it look better--increase contrast a smidge, tone down brightness, whatever--and apply the changes in a 20th of a millisecond.