Astro image processing and machine learning and polsar noise

Modern astronomy requires big data know-how, in particular, highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: astronomy applications touch several current machine learning research questions. will provide far greater data volumes. Another promising future survey is the Large SynopticSurvey Telescope (LSST), which will deliver wide-field images of the sky, exposing galaxies that are too faint to be seen today. A main objective of the LSST is to discover transients, objects that change brightness over timescales of seconds to months. These changes are due to a plethora of reasons, some of which might be regarded as uninteresting while others will be extremely rare events that can’t be missed It s easier to use machine learning for Polysar noise and work for researchers and image processing.

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leraningmachin ، astronomy ، imageprocessing ، polsar