Processing Images#
This section describes the image processing features available in DataLab.
See also
Operations on Images for more information on operations that can be performed on images, or Computing features on Images for information on computing features on images.
When the âImage Panelâ is selected, the menus and toolbars are updated to provide imagerelated actions.
The âProcessingâ menu allows you to perform various processing on the current image or group of images: it allows you to apply filters, to perform exposure correction, to perform denoising, to perform morphological operations, and so on.
Axis transformation#
Linear calibration#
Create a new image which is a linear calibration of each selected image with respect to Z axis:
Parameter 
Linear calibration 

Zaxis 
\(z_{1} = a.z_{0} + b\) 
Swap X/Y axes#
Create a new image which is the result of swapping X/Y data.
Level adjustment#
Normalize#
Create a new image which is the normalized version of each selected image by maximum, amplitude, sum, energy or RMS:
Normalization 
Equation 

Maximum 
\(z_{1} = \dfrac{z_{0}}{z_{max}}\) 
Amplitude 
\(z_{1} = \dfrac{z_{0}}{z_{max}z_{min}}\) 
Area 
\(z_{1} = \dfrac{z_{0}}{\sum_{i=0}^{N1}{z_{i}}}\) 
Energy 
\(z_{1}= \dfrac{z_{0}}{\sqrt{\sum_{n=0}^{N}z_{0}[n]^2}}\) 
RMS 
\(z_{1}= \dfrac{z_{0}}{\sqrt{\dfrac{1}{N}\sum_{n=0}^{N}z_{0}[n]^2}}\) 
Clipping#
Apply the clipping to each selected image.
Offset correction#
Create a new image which is the result of offset correction on each selected image. This operation is performed by subtracting the image background value which is estimated by the mean value of a userdefined rectangular area.
Noise reduction#
Create a new image which is the result of noise reduction on each selected image.
The following filters are available:
Filter 
Formula/implementation 

Gaussian filter 

Moving average 

Moving median 

Wiener filter 
Fourier analysis#
Create a new image which is the result of a Fourier analysis on each selected image.
The following functions are available:
Function 
Description 
Formula/implementation 

FFT 
Fast Fourier Transform 

Inverse FFT 
Inverse Fast Fourier Transform 

Magnitude spectrum 
Optionnal: use logarithmic scale (dB) 
\(z_{1} = FFT(z_{0})\) or \(z_{1} = 20 \log_{10}(FFT(z_{0}))\) (dB) 
Phase spectrum 
\(z_{1} = \angle(FFT(z_{0}))\) 

Power spectral density 
Optionnal: use logarithmic scale (dB) 
\(z_{1} = FFT(z_{0})^2\) or \(z_{1} = 10 \log_{10}(FFT(z_{0})^2)\) (dB) 
Note
FFT and inverse FFT are performed using frequency shifting if the option is enabled in DataLab settings (see Settings).
Thresholding#
Create a new image which is the result of thresholding on each selected image, eventually based on userdefined parameters (âParametric thresholdingâ).
The following parameters are available when selecting âParametric thresholdingâ:
Parameter 
Description 

Threshold method 
The thresholding method to use (see table below) 
Bins 
Number of bins for histogram calculation 
Value 
Threshold value 
Operation 
Operation to apply (> or <) 
The following thresholding methods are available:
Method 
Implementation 

Manual 
Manual thresholding (userdefined parameters) 
ISODATA 

Li 

Mean 

Minimum 

Otsu 

Triangle 

Yen 
Note
The âAll thresholding methodsâ option allows to perform all thresholding methods on the same image. Combined with the âdistribute on a gridâ option, this allows to compare the different thresholding methods on the same image.
Exposure#
Create a new image which is the result of exposure correction on each selected image.
The following functions are available:
Function 
Implementation 
Comments 

Gamma correction 

Logarithmic correction 

Sigmoid correction 

Histogram equalization 

Adaptive histogram equalization 
Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm 

Intensity rescaling 
Stretch or shrink image intensity levels 
Restoration#
Create a new image which is the result of restoration on each selected image.
The following functions are available:
Function 
Implementation 
Comments 

Total variation denoising 

Bilateral filter denoising 

Wavelet denoising 

White TopHat denoising 
Denoise image by subtracting its white top hat transform 
Note
The âAll denoising methodsâ option allows to perform all denoising methods on the same image. Combined with the âdistribute on a gridâ option, this allows to compare the different denoising methods on the same image.
Morphology#
Create a new image which is the result of morphological operations on each selected image, using a disk footprint.
The following functions are available:
Function 
Implementation 

White TopHat (disk) 

Black TopHat (disk) 

Erosion (disk) 

Dilation (disk) 

Opening (disk) 

Closing (disk) 
Note
The âAll morphological operationsâ option allows to perform all morphological operations on the same image. Combined with the âdistribute on a gridâ option, this allows to compare the different morphological operations on the same image.
Edges#
Create a new image which is the result of edge filtering on each selected image.
The following functions are available:
Function 
Implementation 

Roberts filter 

Prewitt filter 

Prewitt filter (horizontal) 

Prewitt filter (vertical) 

Sobel filter 

Sobel filter (horizontal) 

Sobel filter (vertical) 

Scharr filter 

Scharr filter (horizontal) 

Scharr filter (vertical) 

Farid filter 

Farid filter (horizontal) 

Farid filter (vertical) 

Laplace filter 

Canny filter 
Note
The âAll edges filtersâ option allows to perform all edge filtering algorithms on the same image. Combined with the âdistribute on a gridâ option, this allows to compare the different edge filters on the same image.
Butterworth filter#
Perform Butterworth filter on an image (implementation based on skimage.filters.butterworth)
Resize#
Create a new image which is a resized version of each selected image.
Pixel binning#
Combine clusters of adjacent pixels, throughout the image, into single pixels. The result can be the sum, average, median, minimum, or maximum value of the cluster.
ROI extraction#
Create a new image from a userdefined Region of Interest.