Structured illumination microscopy: artefact analysis and reduction utilizing a parameter optimization approach.

Practical applications of structured illumination microscopy (SIM) often suffer from various artefacts that result from imprecise instrumental hardware and certain bleaching properties of the sample. These artefacts can be observed as residual stripe patterns originating from the illumination grating. We investigated some significant causes of these artefacts and developed a correction approach that can be applied to images after acquisition. Most of the artefacts can be attributed to changes in illumination and detection intensities during acquisition. The proposed correction algorithm has been shown to be functional on noisy image data, and produces exceptional, artefact-free results in everyday laboratory work.

Generalized approach for accelerated maximum likelihood based image restoration applied to three-dimensional fluorescence microscopy.

For deconvolution applications in three-dimensional microscopy we derived and implemented a generic, accelerated maximum likelihood image restoration algorithm. A conjugate gradient iteration scheme was used considering either Gaussian or Poisson noise models. Poisson models are better suited to low intensity fluorescent image data; typically, they show smaller restoration errors and smoother results. For the regularization, we modified the standard Tikhonov method. However, the generic design of the algorithm allows for more regularization approaches. The Hessian matrix of the restoration functional was used to determine the step size. We compared restoration error and convergence behaviour between the classical line-search and the Hessian matrix method. Under typical working conditions, the restoration error did not increase over that of the line-search and the speed of convergence did not significantly decrease allowing for a twofold increase in processing speed. To determine the regularization parameter, we modified the generalized cross-validation method. Tests that were done on both simulated and experimental fluorescence wide-field data show reliable results.

A workingperson's guide to deconvolution in light microscopy.

Thefluorescence microscope is routinely used to study cellular structure in many biomedical research laboratories and is increasingly used as a quantitative assay system for cellular dynamics. One of the major causes of image degradation in the fluorescence microscope is blurring. Deconvolution algorithms use a model of the microscope imaging process to either subtract or reassign out-of-focus blur. A variety of algorithms are now commercially available, each with its own characteristic advantages and disadvantages. In this article, we review the imaging process in the fluorescence microscope and then discuss how the various deconvolution methods work. Finally, we provide a summary of practical tips for using deconvolution and discuss imaging artifacts and how to minimize them.