Blind restoration of confocal microscopy images in presence of a depth-variant blur and Poisson noise

We are interested in blind image restoration in confocal laser scanning microscopy (CLSM). Two challenging problems in this imaging system are considered: First, spherical aberrations due to refractive index mismatch leads to a depth variant (DV) blur. Second, low illumination leads to a signal dependent Poisson noise. In addition, the DV point spread function (PSF) is unknown, which increases the complexity of the problem considered. Our goal is to remove in a blind framework both the DV blur and the Poisson noise from CLSM images. Using an approximation of the DV PSF, we define in a Bayesian framework a criterion to be jointly minimized w.r.t. the specimen function and the PSF. We then adopt an alternate minimization scheme for the optimization problem. For each elementary minimization, we use the recently proposed scaled gradient projection (SGP) algorithm that has shown a fast convergence rate. Results are shown on simulated and real CLSM images.