PST or Phase Stretch Transform is an operator that finds features in an image.
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Phase Stretch Transform (PST) is an operator that finds features in an image. PST takes an intensity image I as its input, and returns a binary image out of the same size as I, with 1's where the function finds sharp transitions in I and 0's elsewhere. PST function is also able to return the detected features in gray scale level (i.e. without thresholding).

In PST, the image is first filtered by passing through a smoothing filter followed by application of a nonlinear frequency-dependent phase described by the PST phase kernel. The output of the transform is the phase in the spatial domain. The main step is the 2-D phase function (PST phase kernel) which is typically applied in the frequency domain. The amount of phase applied to the image is frequency dependent with higher amount of phase applied to higher frequency features of the image. Since sharp transitions, such as edges and corners, contain higher frequencies, PST emphasizes the edge information. Features can be further enhanced by applying thresholding and morphological operations. For more information please visit: https://en.wikipedia.org/wiki/Phase_stretch_transform


PST function is developed in Jalali Lab at University of California, Los Angeles (UCLA). PST is a spin-off from research on the photonic time stretch technique in Jalali lab at UCLA. More information about the technique can be found on our group website: http://www.photonics.ucla.edu

This function is provided for research purposes only. A license must be obtained from the University of California, Los Angeles for any commercial applications. The software is protected under a US patent.


  1. M. H. Asghari, and B. Jalali, "Edge detection in digital images using dispersive phase stretch," International Journal of Biomedical Imaging, Vol. 2015, Article ID 687819, pp. 1-6 (2015).
  2. M. H. Asghari, and B. Jalali, "Physics-inspired image edge detection," IEEE Global Signal and Information Processing Symposium (GlobalSIP 2014), paper: WdBD-L.1, Atlanta, December 2014.
  3. M. Suthar, H. Asghari, and B. Jalali, "Feature Enhancement in Visually Impaired Images", IEEE Access 6 (2018): 1407-1415.
  4. Y. Han, and B. Jalali, "Photonic time-stretched analog-to-digital converter: Fundamental concepts and practical considerations", Journal of Lightwave Technology 21, no. 12 (2003): 3085.

Copyright (c) 2016, Jalali Lab All rights reserved.

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Jalali Lab Jalalilab (2024). JalaliLabUCLA/Image-feature-detection-using-Phase-Stretch-Transform (https://www.mathworks.com/matlabcentral/fileexchange/55330-jalalilabucla-image-feature-detection-using-phase-stretch-transform), MATLAB Central File Exchange. Recuperato .

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Le versioni che utilizzano il ramo predefinito di GitHub non possono essere scaricate

Versione Pubblicato Note della release

To make the code easier to understand for users, we put the default feature detection to analog feature detection.

Version 1