Deconvolution — Making the Most of Fluorescence Microscopy

Deconvolution is one of the most common image-reconstruction tasks that arise in 3D fluorescence microscopy. The aim of this challenge is to benchmark existing deconvolution algorithms and to stimulate the community to look for novel, global and practical approaches to this problem.

The challenge will be divided into two stages: a training phase and a competition (testing) phase. It will primarily be based on realistic-looking synthetic data sets representing various sub-cellular structures. In addition it will rely on a number of common and advanced performance metrics to objectively assess the quality of the results.

RICHARDSON-LUCY ALGORITHM

In this challenge, the classical Richardson-Lucy (RL) algorithm will be used as a baseline reconstruction algorithm. To perform the RL reconstruction we provide the RLdeblur3D.m script.

Required Input Arguments

\(y\) : Measured image stack (3D MATLAB array).
\(h\) : Point spread function (3D MATLAB array).

Optional Input Arguments

\(b\) : Scalar value for the background of the image-stack. (Default: 0).
iter: Number of iterations (Default: 100).
tol : Scalar value for the stopping criterion of the algorithm. The stopping criterion is defined as the relative normed difference between two successive iterations (Default: 1e-5).

Output Arguments

\(f\) : The reconstructed image stack.
\(J\) : A vector with the evolution of the objective function.

Deconvolution — Making the Most of Fluorescence Microscopy

RICHARDSON-LUCY ALGORITHM

Required Input Arguments

Optional Input Arguments

Output Arguments

Important Dates

Beginning of training stage