They also made the original and comparison images available to download. The post included several real-life examples of how well the new upscaling feature works.
When upscaling, it can make much better predictions because a red pixel next to a blue pixel can be a completely different type of texture or edge in different images even though, to the primitive approaches, they’re always the same. It takes into account the actual content of every image, attempting to recognize edges, patterns, and textures, recreating detail based on our dataset and extensive training. The Pixelmator Team published a detailed blog post about how they’re using ML to resize raster images while maintaining visual clarity.
Super photo upscaler pro#
How Pixelmator Pro uses Machine Learning ( ML) to upscale images They’re using Apple’s Core ML 3 and neural network capabilities to achieve what was once thought impossible. Pixelmator Pro has released a featured called ML Super Resolution that can make a raster image larger while maintaining its sharpness and detail. Their only choice is to either resize the image and accept that it will appear fuzzier and pixelated, or use a different image. For example, sometimes the only image they have to work with is a rasterized (pixel-based) image that’s 300 px wide, but they need the image to be 600 px wide. Jon Henshaw | | 11:08am CST | ContentĪ common frustration for content producers is to have images that are too small for the page layout.
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