When we create any automatic feature, one of the main goals is for it to never make any image worse. It’s probably fair to say that ML Denoise, our machine learning-powered noise removal feature, has been in a kind of beta stage until today. Floating point operations per pixel (fewer is better): 903,870 (new algorithm) vs. Trainable neural network parameters (more is better): 1,692,928 (new algorithm) vs. This new version of ML Super Resolution does an even better job with the small artifacts and it takes care of the blocks!ġ. They’re most visible in photos that include the sky or other gradients. But JPEGs, especially ones that have been compressed quite a lot, will also feature visible compression blocks Compression Blocks JPEG compression blocks appear when the compression algorithm reduces subtly differently colored pixels (for example, in gradients) to one color. that usually appear around object edges in compressed JPEG images. The previous algorithm already did a great job at removing the small compression artifacts Compression Artifacts Compression artifacts often look like small dots and appear at edge areas - where contrasting colors meet in images. Making the new, smarter algorithm faster and more efficient Compression block removal Preserving sharp edges and details in illustrationsĤ. Improving how portrait photos are upscaledģ. Removing compression blocks in heavily compressed imagesĢ. In this update to it (a version 2.0, if you will) we’ve focused on four things in particular:ġ. ML Super ResolutionĮven though we released it just about two months ago, we’ve made some pretty big improvements to ML Super Resolution in that time. Because seeing is believing, we thought we’d whip up a blog post with some examples to highlight the changes. Howdy! Pixelmator Pro 1.5.5 has just been released and it brings some significant improvements to ML Super Resolution and ML Denoise as well as two new Automator actions for those two features. FebruPixelmator Pro update improves ML Super Resolution and ML Denoise
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