Section: New Results

Applications to biology

Biological image restoration

Keywords : Biological image, confocal microscopy, denoising, restoration.

Participants : Caroline Chaux, Laure Blanc-Féraud, Josiane Zerubia.

This post-doc is partly funded by a CORDI grant and is being done as part of the P2R Franco-Israeli program.

This work is devoted to the restoration of biological images acquired by a confocal microscope. These images are degraded by Poisson noise and blurred due to the acquisition system. Earlier work was dedicated to deconvolution using the Richardson-Lucy algorithm with Total Variation regularization. We focus on restoration using the wavelet transform in order to preserve the texture and fine structures that are usually present in biological images. A 3D complex wavelet transform, proposed by N. Kingsbury, which increases directional selectivity and translation invariance wrt a standard wavelet transform, has already been developed for denoising. We propose to use this transform in the deconvolution process for regularization, together with Total Variation regularization.

Blind biological image deconvolution

Keywords : Biological image, confocal miscroscopy, blind deconvolution.

Participants : Praveen Pankajakshan, Laure Blanc-Féraud, Josiane Zerubia.

This internship was partly funded by an INRIA internship grant and is being done as part of the P2R Franco-Israeli program.

This work is devoted to the automatic estimation of parameters of the PSF (Point Spread Function) modelling the degradation produced during a confocal microscopy acquisition of biological images. It has been shown [30] , [14] that the PSF can be modelled by a Gaussian function. The problem is then to estimate the variance of the Gaussian function from an observed degraded image. We propose to use maximum likelihood to estimate the parameter, taking into account that the noise is Poisson distributed on the observed image. Tests are currently being performed.