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SUMMARY:A new and General Method for Multi-View Deconvolution of Biomedica
 l Images
DTSTART;VALUE=DATE-TIME:20070717T103000Z
DTEND;VALUE=DATE-TIME:20070717T113000Z
DTSTAMP;VALUE=DATE-TIME:20261112T075810Z
UID:indico-event-1586@indico.ific.uv.es
DESCRIPTION:Biomedical images are often degraded during image acquisition.
  Two very common degradations that occur in many biomedical imaging modali
 ties are blur and noise. Corrupted images are more difficult to interpret 
 by the physician or researcher and so the goal is to restore the original 
 image. Blurring can be mathematically modeled by convolution\, and within 
 this model theblur is characterized by the point-spread-function (PSF). Tw
 o kinds of blur can be distinguished: shift-invariant and shift-variant bl
 ur. Shift-invariant blur is constant over the whole image\, and shift-vari
 ant blur is characterized by a PSF that is dependent on the spatial positi
 on in the image. The process of restoring a blurred image is referred to a
 s deconvolution in the literature. There are two major classes: non-blind 
 deconvolution algorithms that assume thatthe PSF is known in advance and b
 lind deconvolution algorithms that do not need any a priori knowledge rega
 rding the PSF. We propose a new blind deconvolution method. As an input\, 
 our method takes several observations of the same scene\, each of them acq
 uired from a different point of view\, and their respective spatial transf
 ormations. The transformationsare either directly known by dedicated image
  acquisition systems\, or they can beretrieved by image registration or tr
 acking. The output is the restored original image and PSF. This multi-view
  setting occurs frequently in practice. However\, most of the methods avai
 lable in the literature do not specifically take advantage of the informat
 ion provided by this setting. We perform a quantitative analysis of our ne
 w method and compare it other deconvolution methods. There are two main co
 ntributions: Firstly\, our algorithm is able to perform unparameterized bl
 ind deconvolution in the shift-variant case. To the best of our knowledge\
 , known methods for blind shift-variant deconvolutions are dependent on an
  a priori blur model. Secondly\, in the shift-invariant case\, our algorit
 hm allows for a better restoration at higher noise levels than comparable 
 methods.\n\nhttps://indico.ific.uv.es/event/1586/
LOCATION:Edf. Institutos de Investigación Sala Reuniones Sotano
URL:https://indico.ific.uv.es/event/1586/
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