Deconvolution Tab

Expert knowledge is required for some of the settings. If you are in doubt, leave the settings unchanged.

Parameter		Description
Algorithm		Select the deconvolution algorithm. The following algorithms are available: Nearest Neighbor Regularized Inverse Filter Fast Iterative Constrained Iterative
Enable Channel Selection		Activated: Applies the settings on a channel specific basis. This allows you to set parameters for each channel individually. A separate, colored tab for each of the channels is displayed. Deactivated: Applies the same settings to all channels of a multichannel image.
Normalization		Specifies how the data of the resulting image is handled if the gray/color levels exceed or fall short of the value range.
–	Clip	Clips the values that exceed or fall short of the value range. Sets negative values to 0 (black). If the values exceed the maximum possible gray value of 65636 when the calculation is performed, they are limited to 65636 (pixel is 100% white). Results from different input images can be quantitatively compared with each other.
–	Automatic	Normalizes the output image automatically. In this case the lowest value is 0 and the highest value is the maximum possible gray value in the image (gray value of 65636). The maximum available gray value range is always utilized fully in the resulting image. Results from different input images cannot directly be compared quantitatively with each other.
–	Factor	Only visible if Clip is selected. Defines a scaling factor for normalization. A factor of 1 or above preserves the original dynamic range information, but high values may be clipped, so the intensity information can be incorrect. A factor below 1 compresses the original dynamic range information, but reduces the clipping effect to keep the pixel intensity relations correct also for bright areas.
Set Strength Manually		For the Nearest Neighbor and the Fast Iterative algorithm, the checkbox is always activated. Activated: Sets the desired degree of restoration with the slider. To achieve strong restoration and best contrast, move the slider towards Strong. To achieve lower restoration but smoother results, move the slider towards Weak. If the setting is too strong, image noise may be intensified and other artifacts, such as "ringing", may appear. Deactivated: Determines the restoration strength for optimum image quality automatically. This is recommended for widefield and confocal images and is therefore deactivated by default. The restoration strength is inversely proportional to the strength of so-called regularization. This is determined automatically with the help of Generalized Cross Validation (GCV).
Convergence History		Visible for Fast Iterative or Constrained Iterative algorithms. Displays the progress of the calculation as line graph. Several quality parameters are measured for each iteration and once either an optimum or the maximum allowed number of iterations is reached, the processing is stopped. This display allows you to observe directly how the iterative method affects the available data. It also shows how many iterations have been used and how much time is being used per iterations.
Corrections		To display parameters for image correction, click .
–	Background	Activated: Analyzes the background component in the image and removes it before the deconvolution calculation. This can prevent background noise being intensified during deconvolution.
–	Bad Pixel Correction	Activated: Employs a fully automatic detection and removal of spurious or hot pixels (also known as stuck pixels) in an image stack which might interfere with the deconvolution result. It is based on the analysis of the gray level variance in the neighborhood of each pixel in the image. It is recommended to use this parameter only, if stuck pixels are observed in the input image.
–	Fluorescent Decay	Activated: Corrects bleaching of the sample during acquisition of the z-stack. This function should only be activated for widefield images. Use it if your sample undergoes strong bleaching during acquisition.
Execution Options		Only visible if Enable Channel Selection is activated. Enables you to select how the respective channel is handled during processing.
–	Execute	Processes this channel according to the set parameters.
–	Copy	This channel is copied into the output image without processing.
–	Skip	Skips the channel when processing. The respective channel will not be in the output image.

Advanced Settings

This section is only visible if you have selected the Regularized Inverse Filter, Fast Iterative or Constrained Iterative algorithm. To display advanced settings, click Blue circular button with white right-pointing triangle .

Parameter		Description
Likelihood		Visible for Fast Iterative and Constrained Iterative algorithms. Selects which likelihood calculation you want to work with.
–	Poisson (Meinel)	Only visible for the Fast Iterative algorithm. The calculation according to Meinel works with one convolution per iteration and converges very fast, normally in 4-5 iterations. This method can also produce artifacts, however.
–	Poisson (Richardson-Lucy)	Only visible for the Fast Iterative algorithm. Accelerated version of Richardson-Lucy, based on a Poisson likelihood and Biggs acceleration (https://doi.org/10.1364/AO.36.001766), allowing for fast and artifact free reconstructions.
–	Poisson	Only visible for the Constrained Iterative algorithm. Computation assuming a Poisson noise distribution, this is normally the correct noise model for microscopic images.
–	Gauss	Only visible for the Constrained Iterative algorithm. Computation assuming a Gaussian noise distribution. If detector noise is dominant over sample noise, using a Gaussian noise model can be advantageous, however, this is rarely the case with modern microscopy systems.
Regularization		Selects which frequencies in the image are taken into account during regularization.
–	None	Only visible for the Fast Iterative and Constrained Iterative algorithms. No regularization is performed.
–	Zero Order	Regularization based on G-difference, modeled on Tikhonov, but accelerated.
–	First Order	Only visible for the Regularized Inverse Filter and Constrained Iterative algorithms. Regularization based on Good's roughness. Under certain circumstances, more details are extracted from noisy data. It may be better suited to the processing of confocal data sets.
–	Second Order	Only visible for the Constrained Iterative algorithm. Regularization according to Tikhonov-Miller. Here higher frequencies are penalized more than in the case of Good's roughness. Results have a tendency to become overly smoothed.
–	Total Variation	Only visible for the Fast Iterative algorithm. Total Variation regularization denoises the input data but protects the edges of structures. This can improve results for noisy data but increases the computation time quite significantly.
Optimization		Visible for Fast Iterative and Constrained Iterative algorithms.
–	Analytical (Newton Raphson)	Only visible for the Constrained Iterative algorithm. Here an attempt is made to optimize the iterations analytically. It is an optimization method to find the step size in each of the Constrained Iterative iterations. This option is usually faster but may also be somewhat less precise.
–	Line Search	Only visible for the Constrained Iterative algorithm. Searches rigorously and comprehensively for the minimum. It is therefore more robust, but the calculation takes longer. It is an optimization method to find the step size in each of the Constrained Iterative iterations. Line search is recommended for confocal data sets, especially if they are noisy as this can enforce convergence even for noisy and sparsely sampled data.
–	Numerical Gradient	Only visible for the Fast Iterative algorithm. If selected, an attempt is made to determine the trend of the iterations in advance and extrapolate this to the entire calculation. This can significantly speed up the calculation.
First Estimate		Visible for Fast Iterative and Constrained Iterative algorithms.
–	Input Image	The input image is used as the first estimate of the target structure (default).
–	Last Result Image	The result of the last calculation is used to estimate the next calculation. This can speed up a calculation that is repeated using slightly different parameters.
–	Mean of Input	No estimate is made, the mean gray level of the input image is being used. This is the most rigid application of deconvolution. It should be chosen for confocal images, where the data sampling can be quite sparse. The computation time will increase, but missing information can be recovered from the PSF.
–	Zero Values	Only visible for the Constrained Iterative algorithm.
Maximum Iterations		Visible for Fast Iterative and Constrained Iterative algorithms. Sets the maximum permitted number of desired iterations. In the case of Richardson-Lucy, you should allow significantly more iterations here.
Quality Threshold		Only visible for the Fast Iterative and Constrained Iterative algorithms. Defines the quality level at which you want the calculation to be stopped. The percentage describes the difference in enhancement between the last and next-to-last iteration compared with the greatest difference since the start of the calculation. 1% is the default value. Lowering this can bring about small improvements in quality.
GPU Acceleration		Only visible if a suitable (NVIDIA, CUDA based) graphics card is installed in your PC. The checkbox is then activated by default. Activated: Uses GPU processing. Deactivated: Uses CPU processing.
GPU Tiling		Only available for very large images that exceed the available graphic card memory. Activated: With this function the image is split up in smaller portions which fit into the memory of the graphic card. The function automatically determines into how many tiles the image must be split to allow maximum usage of the graphics card. The resulting tiles are automatically stitched together for the final output result. Deactivated: No tiling is performed, however, in this case only certain sub-functions of deconvolution can run on the graphics card and the speed increase compared to CPU processing will be lower. The image quality might be higher than with tiling because there is no need for stitching.

Deconvolution Tab

Advanced Settings

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