New AI Can Retouch Your Photos Before Capture

New AI Can Retouch Your Photos Before Capture”

Apps like Camera+, Instagram and Snapchat have made it fairly easy to turn decent mobile photos into something a bit more appealing to the eye, but they're a far cry from the retouching efforts of professional photographers. When researchers conducted the processing, it was found that the low-resolution image required 100 megabytes of memory to perform the operations, whereas a high-resolution version required almost 12 gigabytes, which means it would take longer to process a full-resolution image as the standard algorithm, according to MIT. In this case, researchers used a data set they created in cooperation with Adobe Systems which included 5000 images that had each been colour-corrected and retouched by five different professional photographers, allowing the neural network to generate its own guidelines on what the final result should look like given a specific starting image.

Photographers will no longer have to spend time balancing the colours or tuning the photograph after taking the image.

The same system can also speed up existing image-processing algorithms.

"This paper may provide us with a way to sidestep these issues and produce new, compelling, real-time photographic experiences without draining your battery or giving you a laggy viewfinder experience", Barron added. Previous approaches performed the retouching on a low-resolution version of the source image to speed up the process, but trying to up-res the results inevitably produces awful looking photos.

Using machine learning to improve photos has been done before, but the real advance with this research is slimming down the algorithms so that they are small and efficient enough to run on a user's device without any lag.

The system is able to retouch images in real time as it doesn't apply its image editing algorithm to the high-res image until it's done some maths on a lower-resolution version first.

To make the changes on the pixels in the high resolution version that were not present in the low resolution version, a combination of the formulae is applied according to the distance of the pixels from those that were present in the low resolution version. The researchers conducted the image processing on a photo with low resolution to save energy, though this work compromised the color values of each pixel on the image in the resulting high-resolution image.

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