![]() Here’s an example: Left to right: Original Nearest Neighbor Bilinear Lanczos ML Super Resolution.īehind the scenes, the magic is done by machine learning, which attempts to recognize edges, patterns, and textures, and then recreate detail based on its dataset and extensive training. Sometimes the improvements are modest other times the improvements are striking but, in every case, ML Super Resolution looked better to my eye. I tried it with half a dozen different images, and it does a better job of enlarging (and reducing) the dimensions and resolution of photos than other available algorithms. While it can’t enlarge a postage-stamp sized low-resolution image to a perfect poster-sized high-res image, I’m amazed at how well the machine learning algorithm works to double, triple, or even quadruple the size and resolution of many images. ![]() ![]() I’m not sure I’d go that far, but after a couple of days of testing, I’m quite impressed. The blog post announcing the new feature proudly proclaimed, “Yes, zooming and enhancing images like they do in all those cheesy police dramas is now a reality!” Last week, however, Pixelmator Pro introduced a breakthrough feature called ML Super Resolution that uses machine learning to increase the resolution (size) of an image without losing (much) detail or introducing unwanted artifacts. If you try to enlarge or reduce most images using those algorithms, the result will look better than if no algorithm were applied, but will rarely look great. ![]() Over the years graphics apps have improved at scaling images using algorithms with names like Bilinear and Nearest Neighbor, but the results have never been great. If you’ve been manipulating images with a computer as long as I have, you know that the holy grail for image-processing apps like Photoshop, Affinity Photo, and Pixelmator Pro is to enlarge (or reduce) an image without introducing visible artifacts, blurriness, jagged edges, or other unwanted elements. ![]()
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