![]() Which one is correct, or at least a better guess?Īll deconvolution algorithms use knowledge of an image’s point spread function (PSF). Mathematically, deconvolution is said to be an ill posed problem: for a given blurry input image, there are many possible sharper images that, if re-blurred, would result in the same input image. Great care has been taken in the architecture and training of the neural network to ensure that its output is as faithful as possible to reality if it is properly used.Īll deconvolution, including the classical algorithms developed by Richardson, Lucy, van Cittert, and others, fundamentally involves guesswork. The design intent of BlurXTerminator is to recover as much detail as possible based on low-contrast information actually present in an image, without fabricating detail that does not in fact exist just for the sake of an image that appears sharper. ![]() They don’t usually handle stars very well, and because they are trained as generative AI models, they have no regard for veracity and often invent detail that does not exist. AI-based sharpening tools for general photography exist, but they were not trained with astronomical images in mind. Because deconvolution inherently requires linear image data, BlurXTerminator will not be made available for general photography applications such as Photoshop. It is available as a plug-in process module for PixInsight only. BlurXTerminator is an AI-powered deconvolution tool designed specifically for astronomical images.
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