2D DFT Filter
2D DFT Filter Butterworth BP
2D DFT Filter Butterworth BP Tiled
2D Kernel Convolution
2D Median
2D Morphological Operator
2D Nonlinear Filter
2D Savitzky-Golay filter
Absolute Value
Affine Transformation
Align Channels
Align Series (Image Stabilizer)
Align Tiled Channels
Align Tiled Series (Image Stabilizer)
Anisotropic Diffusion Filter
Attach Intensity Gating Image
Attach Overlay Image
Automatic ROI drawing
Band Pass filter Optimization
Blind Spectral Unmix with NMF
Calculate Simple Crossbleed
Calculate Spatial Moments
Calibration Wizard Parameters
Clear Segmentation Classifiers
Close Image Window
Copy Image Window
Copy ROIs from Other Image
Correct Intensity Jump
Correct Lens Distortion
Count Division and Cell Death
Count Object Colocalization
Create ROI
Create ROIs from Segments
Crop
Crop Image in Place
Crop Image to Segments
Cross-correlation data
Cross-correlation image
Detect Nuclei Convolution
Differential Evolution Optimizer
Distance from Segments
Draw Model Mitochondrion
Draw Random Position Model Mitochondria
EndIf
Erase All ROIs
Excel Window Command
Export
Fill Mask
Fill or Mask Active ROI
FLIPR Calibration with [K+]ec steps and known [K+]ic and known kP
FLIPR Complete Calibration
FLIPR Complete Calibration with known kP
FLIPR Complete Calibration with known kP - Goldman
FLIPR Complete Iterative Calibration
FLIPR Estimate PN
FLIPR Short Calibration based on known potential during MDC and CDC
FLIPR Short Calibration between known baseline and CDC
FLIPR Short Calibration between known baseline and MDC
FLIPR Short Calibration between known baseline and separately measured fP0
FLIPR Short Calibration from Zero with fx=0
FunctionOptions
Get Image Information
Get Linked Channel
If
If Greater Than Zero
Image Arithmetic (image-image math)
Image Arithmetic In Place
Image Arithmetic Single Frame
Inpaint Mask
Input
Invert
Lens Correction Optimization
Link Image Windows
Load and Run Pipeline
Load ROIs
Mask Borders
Mask Frames by Plot Values
Mask Images
Measure Object Intensity
Measure Object Morphology
Mirror or Rotate (new image)
Mirror or Rotate in Place
Multi-Dimensional Open Information
Multi-Dimensional Open Stage Position
Multi-Dimensional Reload Channel
New Image
New Time Scale
Onset Image
Open File
Optical Flow
Options
Pipeline
Pipeline Optimization
Pipeline Optimization Parameter
Plot
Plot Correlation (Colocalization)
Plot Intensities Corresponding to Segments
Plot Morphological Parameters of Segments
Plot Ratio
Plot ROI Dimensions
Plot Tracking Parameters
Potential calibration constants
Potential calibration error propagation
Potential calibration expert overrides
Projection of Vectors from a Point
Ratio
Ratiometric ROI Classifiers
ReCount Division and Cell Death
Reevaluate Segments
Remove Blank Frames
Rename
Resample Image
ROI Classifiers
Run Membrane Potential Calibration
Save ROIs
Scalar Arithmetic (image-value math)
Scalar Arithmetic Multi
Secondary Watershed Segmentation
Select
Select by Number
Sensor Noise Characteristics
Set Reference Image
Set Scaling/LUT
Set Segmentation Classifiers
Set Segmentation Intensity Classifiers
Shift Time Scale
Simple 2D Cross-correlation
Simple Segmentation
Skeletonize
Spectral Unmix
Strip to Well Cell Count
Substitute Poisson Noise
Subtract Background or Normalize
T or Z-project
Template Matching
Temporal Average Filter
Temporal Block Filter
Temporal Median Filter
Temporal Rolling Projection
Temporal Savitzky-Golay Filter
Thinness Ratio Optimization
Threshold
Time Stamp and Scale Bar
TMRM Complete Calibration
TMRM Complete Calibration with known kT
TMRM Complete Calibration with known kT and K-steps
TMRM Short Calibration between known baseline and MDC or CDC
Track Objects
Truncate or Cut
Wait for All Inputs
Watershed Segmentation
Wiener filter
Window Menu Command
Write Back Scaled Values
ΔF/F0

Sensor Noise Characteristics ( IANoiseCharacteristics )

Parameters:

Name	Short Name	Type	Description
Set values in Optical Flow functions	optflow	boolean	Results are automatically transferred to the default parameters of Optical Flow functions.

Description:

To calculate sensor noise characteristics for Optical Flow, use an image series showing evenly illuminated view fields at frame-to-frame increasing intensities, from complete darkness to close to saturation. Noise characteristics are calculated from the intensity-variance relationship of Poisson noise.
The generated trace has to be linear. It can be curved because of inhomogeneous illumination. Try to use smaller ROI, and move it around the image until finding the minimal slope value.
ROI: only pixels in the given ROI are used for the calculation
Set values in Optical Flow functions: values are automatically filled in the Optical Flow functions.
Results: minimal intensity and variance corresponding to the offset of the sensor and the readout noise. The slope corresponds to photoelectrons per gray value unit.