Pixelmator Pro’s ability to treat images with machine learning isn’t limited to increasing resolution, it also can be used to remove compression artifacts or noise from low low light, results of highly compressing an image with a lossy format like JPEG. Machine learning will not provide as good as results as using higher resolution image, but when you do not have the option of a higher resolution source Machine learning will provide much better results. This series of images demonstrates different algorithms used for upscaling an image 3x times it’s original size. It will then infer that it should try and keep the sharpness rather than lose the detail, when it creates new pixels to fill the space from upscaling. If you have a sharp line, perhaps a sharp mountain silhouette against a sunset (or in my case, a flower and a bug), the machine-learning algorithm will “notice” the sharp contrast between the two areas as it has been “trained” to do so. Before machine-learning, upscaling meant duplicating pixels (nearest neighbor) or duplicating pixels and creating transitions between the hue/luminosity use an algorithm (bilinear or lanczos). These use tricks like taking into account the hue/luminosity (color and its brightness) of surrounding pixels and filling in what it believes the new pixels should be. Upscaling has come a long way in the past few years, with machine-learning-assisted upscaling algorithms. This happens when you zoom in on an image in an application like Preview or Photoshop zoom in on a web browser or pinch and zoom on your iPhone or play a lower resolution video on a high resolution screen like 1080p video on a 4k monitor. One of the very frequent tasks computers do is upscaling raster images, aka zooming in or increasing the size of an image. Instead they only take a single click of a mouse. These might not be that big of a deal if they took an esoteric knowledge set to use. ![]() ![]() Today there are viable alternatives to Photoshop and my personal favorite is Pixelmator Pro, a much more nimble image editor that packs in some game changing features, many of which are based on machine learning, like ML Super Resolution. The hardest application for me to replace was Photoshop, as I’ve been using Photoshop since version 3.0 as a kid, as in the PowerPC Photoshop 3.0 from the early 90s, not CS 3, before Photoshop could be used as an adjective. In recent years, my personal and professional life has shifted away from Adobe Creative Cloud to a more varied and diverse software set. Rather than discuss the ins and outs of machine learning, how its shaped speech recognition software or computer vision, we can make it work for us without a deep understanding. Now we’re in the era of machine learning which can do a lot of dirty work for us.Įxplaining machine learning for the non-programmers of the world generally causes one’s eyes to glaze over, explaining training data, neural networks, probabilistic reasoning and so forth but it has become so ubiquitous even non-programmers encounter it’s shorthand, ML. ![]() Previously taking on a project like this, would have required tens of hours restoring them by hand painting in lost details to achieve similar results per photo, whereas today it a fraction of that, spending only an hour or so per photo as I being meticulous and still performing minor touch ups. The big question I kept getting asked was, how do did I do it? ![]() Recently, I restored a bunch of old macOS (OS X) desktop backgrounds from OS X 10.6 Snow Leopard (2006) on my personal blog, upscaling them from their original 1920 x 1200 resolutions to 5k, likely much higher than even the original photos ever existed.
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