Available in the App Store, Pixelmator includes advanced image editing features like layers, brushes, effects, filters, and more. Pixelmator is a great photo editor app alternative to Adobe Photoshop for iPhone, iPad, and Mac.
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The company warns that Pixelmator Pro document support is limited at present but promises more to come. For instance, Adobe also has its own super-resolution tool in its Adobe Camera suite, but Pixelmator’s tool seems to produce the most consistently high-quality images.Pixelmator 2.7 is out today with a modern redesign, support for Pixelmator Pro documents, and performance improvements, thanks to the use of Metal graphics. The training dataset is fairly small when compared to other datasets used for similar applications, just 15000 samples were used to train the algorithms.Īccording to The Verge, there are other super-resolution tools available for use by consumers.
Pixelmator’s algorithms are much smaller than the algorithms used in research settings, in order that they can be included in the Pixelmator Pro app and run on a variety of devices. The network also contains functions that denoise the image and deal with compression artifacts, so that these aspects of the image are not upscaled. The enlarged image array is then post-processed and transformed back into a traditional image with improved resolution. In the case of Pixelmator’s Super-Resolution tool, a convolutional neural network was created that also implemented an “enlarge” block which upscales the image after the 29 convolutional layers scan the image. The network that creates the fakes is called the generator, while the network that detects them is the discriminator. Essentially, one neural network’s job is to create fake images, while the job of the other network is to detect these fake images. GANs are actually two neural networks pitted against one another, borrowing concepts from Game Theory like the zero-sum game and the actor-critic model. For instance, one method of super-resolution is the use of Generative Adversarial Networks (GANs). Super-resolution applications can be created with a variety of methods. It can then use these patterns of difference to predict where to add pixels to an image to improve resolution when it is presented with an unseen image. The goal is that the neural networks learn to distinguish patterns of pixels that will lead to a higher resolution image. Comparisons are made between the low-resolution and high-resolution images, and the machine learning algorithms learn how the regions of pixels in the high-resolution images are different from the low-resolution images. The low-resolution images are typically just scaled-down versions of the regular, high-resolution images. ML Super Resolution, and other super-resolution tools, are trained using pairs of low-resolution and high-resolution images. Multiple companies have designed their own super-resolution algorithms, but the method used to train the difference super-resolution devices uses the same basic principles. Research conducted into super-resolution has been driven by a variety of tech companies like Google, Microsoft, and Nvidia. As reported by The Verge, the results created by the program also seem to be better than other image upscaling tools, which often utilize algorithms such as Nearest Neighbors and Bilinear algorithms.
Pixelmator’s demonstration of some of the results can be seen here.Įarly tests of the tool show that it is able to reduce blur in multiple types of images, including text, photographs, and illustration. Pixelmator recently announced the inclusion of its “ML Super Resolution” tool in the Pro version of its photo editing software. Super-resolution technology is able to sharpen images to impressive effect, often evoking the “enhancing” trope that is often seen in crime shows. Super-resolution enables blurry, low-resolution photos to be enhanced and the resolution of the image improved. Pixelmator has recently enabled owners of Pixelmator Pro, a photo manipulation app, to make use of a super-resolution tool powered by AI.
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