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Titlebook: Inside PixInsight; Warren A. Keller Book 20161st edition Springer International Publishing Switzerland 2016 Astro-image processing.Astrono

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書目名稱Inside PixInsight
編輯Warren A. Keller
視頻videohttp://file.papertrans.cn/468/467789/467789.mp4
概述Chosen as a Sky and Telescope Hot Product in 2018!.Authored by the foremost expert on the software PixInsight, having created a series of successful instructional video tutorials.Augmented by screensh
叢書名稱The Patrick Moore Practical Astronomy Series
圖書封面Titlebook: Inside PixInsight;  Warren A. Keller Book 20161st edition Springer International Publishing Switzerland 2016 Astro-image processing.Astrono
描述In this book, Warren Keller reveals the secrets of astro-image processing software PixInsight in a practical and easy to follow manner, allowing the reader to produce stunning astrophotographs from even mediocre data. As the first comprehensive post-processing platform to be created by astro-imagers for astro-imagers, it has for many, replaced the generic graphics editors as the software of choice.?With clear instructions from Keller, astrophotographers can get the most from its tools to create amazing images. ?Capable of complex post-processing routines, PixInsight is also an advanced pre-processing software, through which astrophotographers calibrate and stack their exposures into completed master files.?Although it is extremely powerful, PixInsight has been inadequately documented in print--until now.?With screenshots to help illustrate the process, it is a vital guide.
出版日期Book 20161st edition
關鍵詞Astro-image processing; Astronomical image software; Astrophotograph tricks; Deep-sky image editing; Dig
版次1
doihttps://doi.org/10.1007/978-3-319-25682-5
isbn_ebook978-3-319-25682-5Series ISSN 1431-9756 Series E-ISSN 2197-6562
issn_series 1431-9756
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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Star Alignmentexposures into a single master frame, the features of the subjects in the individual exposures must lay atop their counterparts in the other images with sub-pixel accuracy, before they’re integrated. In the absence of stars—such as in lunar, solar, or planetary imaging, a correlation of surface feat
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Introduction to Maskstecting others. An example of this would be the ability to recover fine detail in areas of high-signal such as the core of a galaxy or nebula, without the risk of sharpening noise in areas of low-signal such as the background sky. We’ve reached a point in our processing workflow where masks are need
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Linear Color Processing and Noise Reductiont background modelization greatly improved color balance (Chap. .). With the majority of nasty pink and cyan light pollution gradients gone, you’d be closer to attaining true color. A one-shot color master would now be ready for three more steps toward great color balance. Before we see them, let’s
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