ERC Project iCON
Integrating real-time and post-processing Image Restoration
The iCON project aims at the development of new computational tools for image restoration by real-time feedback control with full images recorded by a CCD camera. The innovation pursued with these new tools is their computational complexity that is linear in the degrees of freedom and/or can be implemented on a distributed array of multi-cores. iCON will enable to break away from the existing quasi-static Adaptive Optics (AO) or off-line phase diversity approaches.
Programme Components
The improvements over these existing image restoration methods are a consequence of three innovative steps taken in this project:
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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.
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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.
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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.
Key enabling technology
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.
Demonstrators
The new algorithmic developments will be demonstated in 2 state of the art laboratory demonstrators that will be built up in the Smart Optics Laboratory of Prof. Michel Verhaegen. The first demonstrator is a breadboard emulating the large dimensionality of the Adaptive Optics control problem in the European Extreme Large Telescope and the second is about multi-photon microscopy.
Related research projects
Project members
- Prof. dr. ir. Michel Verahegen
- Prof. dr. Gleb Vdovine
- dr. Paolo Pozzi
- dr. Hieu Thao Nguyen
- ir. Pieter Piscaer
- ir. Baptiste Sinquin
- ir. Dean Wilding
ERC Sponsoring
iCON is sponsored by the Advanced Grant Program of the European Research Council. This funding will bring a core team of 6 temporary researchers together with world wide leading experts for a period of 5 years starting early 2014.
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- Model-based wavefront sensorless adaptive optics system for large aberrations and extended objects.
Huizhen Yang, Oleg Soloviev, and Michel Verhaegen. Opt. Express 23, 24587-24601 (2015). - Holographic imaging with a Shack-Hartmann wavefront sensor. PDF
Hai Gong, Oleg Soloviev, Dean Wilding, Paolo Pozzi, Michel Verhaegen, and Gleb Vdovin. Opt. Express 24, 13729-13737 (2016). - Pupil filters for extending the field-of-view in light-sheet microscopy. PDF
Dean Wilding, Paolo Pozzi, Oleg Soloviev, Gleb Vdovin, Colin J. Sheppard, and Michel Verhaegen. Opt. Lett. 41, 1205-1208 (2016). - N2SID: Nuclear norm subspace identification of innovation models. PDF
Michel Verhaegen and Anders Hansson Automatica Volume 72, October 2016, Pages 57–63. - Subspace Identification of Distributed Clusters of Homogeneous Systems PDF
Chengpu Yu and Michel Verhaegen. Accepted by IEEE Transactions on Automatic Control (2016). doi: 10.1109/TAC.2016.2555961. - Quantized identification of ARMA systems with colored measurement noise. PDF
Chengpu Yu, Keyou You, and Lihua Xie. Automatica, vol. 66, pp 101-108, 2016 - Lensless coherent imaging by sampling of the optical field with digital micromirror device. PDF
G Vdovin, H Gong, O Soloviev, P Pozzi, and M Verhaegen.Journal of Optics, Volume 17, Number 12, 2015 - Blind multivariable ARMA subspace identification. PDF
Chengpu Yu, and Michel Verhaegen. Automatica, vol. 66, pp 3-14, 2016
- Model-based wavefront sensorless adaptive optics system for large aberrations and extended objects.
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- Distributed System Identification with ADMM.
Anders Hansson and Michel Verhaegen. 53rd IEEE Conference on Decision and Control, Los Angeles, CA, 2014, pp. 290-295. - Optical field reconstruction with digital micromirror interferometry.
Hai Gong,Paolo Pozzi,Oleg Soloviev, Michel Verhaegen, and Gleb Vdovin. 53rd IEEE Conference on Decision and Control, Los Angeles, CA, 2014, pp. 290-295. - Curvature sensing with a Shack-Hartmann sensor (conference abstract, page 18).
Hai Gong,Paolo Pozzi,Oleg Soloviev, Michel Verhaegen, and Gleb Vdovin. 53rd IEEE Conference on Decision and Control, Los Angeles, CA, 2014, pp. 290-295. - Subspace identification of 1D spatially-varying systems using Sequentially Semi-Separable matrices.
B. Sinquin and M. Verhaegen. 2016 American Control Conference (ACC), Boston, MA, 2016, pp. 54-59. - Light-sheet optimization for microscopy.
Dean Wilding , Paolo Pozzi , Oleg Soloviev , Gleb Vdovin , Michel Verhaegen. Proc. SPIE 9717, Adaptive Optics and Wavefront Control for Biological Systems II, 971718 (March 15, 2016); - Wavefront coding with adaptive optics.
Temitope E. Agbana , Oleg Soloviev , Vitalii Bezzubik , Vsevolod Patlan , Michel Verhaegen , and Gleb Vdovin. Proc. SPIE 9335, Adaptive Optics and Wavefront Control for Biological Systems, 93350Q (March 10, 2015); - Sensorless adaptive optics system based on image second moment measurements.
Temitope E. Agbana ; Huizhen Yang ; Oleg Soloviev ; Gleb Vdovin ; Michel Verhaegen; Proc. SPIE 9896, Optics, Photonics and Digital Technologies for Imaging Applications IV, 989609 (April 29, 2016); - Identification of Structured LTI MIMO State-Space Models.
Chengpu Yu, Michel Verhaegen, Shahar Kovalsky, and Ronen Basri. IEEE Conference on Decision and Control, 2015. - Local Subspace Identification of Distributed Homogeneous Systems With General Interconnection Patterns.
Chengpu Yu, and Michel Verhaegen. IFAC Symposium of System Identification. - Subspace identification of local 1d homogeneous systems.
Chengpu Yu, Michel Verhaegen, and Anders Hansson. IFAC Symposium of System Identification, 2015. - Sequential convex relaxation for convex optimization with bilinear matrix equalities
Reinier Doelman, Michel Verhaegen. Proceedings of the European Control Conference 2016. - Adaptive optics in digital micromirror based confocal microscopy.
Paolo Pozzi, Dean Wilding, Oleg Soloviev, Gleb Vdovin, and Michel Verhaegen Proceedings of SPIE VOLUME 9717 - Adaptive Optics and Wavefront Control for Biological Systems II.
- Distributed System Identification with ADMM.