Deep Neural Networks for Aberrations Compensation in Digital Holographic Imaging of the Retina

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Abstract

In computational imaging by digital holography, lateral resolution of retinal images is limited to about 20 microns by the aberrations of the eye. To overcome this limitationthe aberrations have to be canceled. Digital aberration compensation can be performed by post-processing of full-field digital holograms. Aberration compensation was demonstrated from wavefront measurement by reconstruction of digital holograms in subapertures, and by measurement of a guide star hologram. Yet, these wavefront measurement methods have limited accuracy in practice. For holographic tomography of the human retina, image reconstruction was demonstrated by iterative digital aberration compensationby minimization of the local entropy of speckle-averaged tomographic volumes. However image-based aberration compensation is time-consuming, preventing real-time image rendering. We are investigating a new digital aberration compensation scheme with a deep neural network to circumvent the limitations of these aberrations correction methods. To train the network, 28.000 anonymized images of eye fundus from patients of the 15-20 hospital in Paris have been collected, and synthetic interferograms have been reconstructed digitally by simulating the propagation of eye fundus images recorded with standard cameras. With a U-Net architecture, we demonstrate defocus correction of these complex-valued synthetic interferograms. Other aberration orders will be corrected with the same methodto improve lateral resolution up to the diffraction limit in digital holographic imaging of the retina.

Documents

Bibtex (lrde.bib)

@InProceedings{	  rivet.19.spie,
  author	= {Julie Rivet and Guillaume Tochon and Serge Meimon and
		  Michel P\^aques and Thierry G\'eraud and Michael Atlan},
  title		= {Deep Neural Networks for Aberrations Compensation in
		  Digital Holographic Imaging of the Retina},
  booktitle	= {Proceedings of the SPIE Conference on Adaptive Optics and
		  Wavefront Control for Biological Systems V},
  month		= feb,
  year		= 2019,
  address	= {San Francisco, CA, USA},
  abstract	= {In computational imaging by digital holography, lateral
		  resolution of retinal images is limited to about 20 microns
		  by the aberrations of the eye. To overcome this limitation,
		  the aberrations have to be canceled. Digital aberration
		  compensation can be performed by post-processing of
		  full-field digital holograms. Aberration compensation was
		  demonstrated from wavefront measurement by reconstruction
		  of digital holograms in subapertures, and by measurement of
		  a guide star hologram. Yet, these wavefront measurement
		  methods have limited accuracy in practice. For holographic
		  tomography of the human retina, image reconstruction was
		  demonstrated by iterative digital aberration compensation,
		  by minimization of the local entropy of speckle-averaged
		  tomographic volumes. However image-based aberration
		  compensation is time-consuming, preventing real-time image
		  rendering. We are investigating a new digital aberration
		  compensation scheme with a deep neural network to
		  circumvent the limitations of these aberrations correction
		  methods. To train the network, 28.000 anonymized images of
		  eye fundus from patients of the 15-20 hospital in Paris
		  have been collected, and synthetic interferograms have been
		  reconstructed digitally by simulating the propagation of
		  eye fundus images recorded with standard cameras. With a
		  U-Net architecture, we demonstrate defocus correction of
		  these complex-valued synthetic interferograms. Other
		  aberration orders will be corrected with the same method,
		  to improve lateral resolution up to the diffraction limit
		  in digital holographic imaging of the retina.}
}