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.

FORWARD-MODEL IMPLEMENTATION

The forward model that we adopt in the deconvolution challenge can be mathematically described as $$\mathbf{y}=Q\left(P\left(\mathbf{K}\mathbf{x}+\mathbf{b}\right)+\mathbf{w}\right),$$ where $$\mathbf{K}\in\mathrm{R}^{N\times N}$$ is a matrix that models the point spread function (psf) of the microscope, $$P$$ is an operation that describes the Poisson noise, $$\mathbf{y},\mathbf{x}\in\mathrm{R}^N$$ are the vectorized versions of the observed and ground-truth image stacks, respectively, $$\mathbf{b}\in\mathrm{R}^N$$ is a constant vector which models the image background and $$\mathbf{w}\in\mathrm{R}^N$$ represents additive i.i.d Gaussian noise. Finally, $$Q$$ is a function which quantizes the final output.

To produce the synthetic degraded measurements according to the above forward model we provide the function ForwardModel3D.m. Below there is a description about the input and output arguments of this script.

Required Input Arguments

• $$x$$ : Ground-truth image stack (3D MATLAB array).
• $$h$$ : Point spread function (3D MATLAB array).
• $$k$$ : Scalar value with the desired average photons per voxel for the blurred version of the ground-truth image stack.

Optional Input Arguments

• $$b$$ : Scalar value for the background of the image-stack. (Default: 0)
• $$\sigma$$ : Standard deviation for the Gaussian noise. (Default: 0)

Output Arguments

• $$y$$ : The degraded image stack according to the observation model (3D MATLAB array).
• $$f$$ : The normalized ground-truth image stack (3D MATLAB array). The normalization is performed according to the chosen average photons per voxel. It is necessary so as to ensure that the evaluation of the reconstructions is corerctly performed.
• $$f_b$$ : MATLAB 3D array which corresponds to the intermediate result $$\mathbf{K}\mathbf{x}+\mathbf{b}$$ of the observation model. This output is used as an input in one of the used quality metrics.

Important Dates

Beginning of training stage

The training stage of the 2nd edition of the challenge will begin soon. Follow this link for early registration.

July 15, 2013