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Summary Of Se D Da Ra
 

Summary Of Se D Da Ra

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    Summary Of Se D Da Ra Summary Of Se D Da Ra Presentation Transcript

    • Matsuda Kazuki 2009/9/5
    • Written by Yoshifumi Sudo, Naoshi Baba, Noriaki Miura, Satoru Ueno, and Reizaburo Kitai
      • Shift and Add Operation is very useful to get high-resolution image from atmospherically degraded data.
      • The self-deconvolving data reconstruction algorithm (SeDDaRA) is to augment high-speed-frequency components in solar speckle images.
      • Self-deconvolving image -> SAA -> get High-resolution image.
      • Adaptive Optics mitigate atmospheric turbulence in real time.
      • On the other hand, there are various a posteriori methods.
        • Stellar speckle imaging – SAA is simplest in this category.
            • Can applied to solar image.
      • Key point of SAA method is shifting image on point position of the instantaneous PSF.
        • But it is difficult to apply to extended object.
      • Some alternative method is required to find shifting width.
        • Co-relation. On Granulation. From reference frame.
      • But, in this way, quality of SAA result is determined by selection of reference frame.
      • It is desirable that human’s selection independent results (& method).
      • 1 st – self-deconvolving.
      • 2 nd – SAA with reference frame.
        • Reference frame is determined from RMSC.
      • The i-th specle image g i (x,y) is writen
      • The Fourier Transform is
      • Deconvolution by using a “pseudo Wiener filter Di(u,v)”
      • H is low frequency of G-N
      • SAA
      • Reference frame is determined by RMSC
      • A Frame which had Highest RMSC is reference frame.
      • Fix reference frame and SAA method.
      • One more SAA method under the condition that reference frame is 1 st result.
      • Finish.
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