My goal is to develop new computational tools for image restoration by real-time feedback control with full images recorded by a CCD camera. iCON will enable to breakaway from the existing quasi-static Adaptive Optics (AO) or off-line phase diversity approaches. The improvements over these existing image restoration methods are a consequence of three innovative steps taken in this project. The first is the modelling through system identification of the coupled dynamics between the temporal and spatial varying dynamics of the wavefront aberrations that blur the images. New multidimensional distributed Subspace Identification methods will be developed to derive mathematical models that predict the coupled dynamics of the total imaging plant. The use of subspace identification will enable to extract accurate prediction models since no a priori model parameterization is needed, since no use is made of nonlinear parameter optimization and since use can be made of closed-loop data. The accurate predictions are used in the real-time feedback controller to correct the aberrations when they actually occur. The second is the enabled use of the CCD image recording for both identification and real-time control. This sensor provides much more detailed information on the wavefront aberration and the object compared to classically used AO pupil wavefront sensors, e.g. a Shack-Hartmann. The third is the coupling between real-time image restoration and post-processing whereby the real-time feedback provides accurate prior information for the complicated nonlinear optimization in post-processing. The new iCON methodology will enable to consider spatio-temporal feedback on the total imaging plant from the onset of the instrument design cycle. This will lead to finding a better balance between imaging resolution on one hand and size, cost and complexity on the other. Therefore iCON will be a key enabling technology for developing low cost high resolution imaging instruments.
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