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SPUD: simultaneous phase unwrapping and denoising algorithm for phase imaging.

Jesus Pineda, Jorge Bacca, Jhacson Meza

|May 14, 2020

Summary

This summary is machine-generated.

We developed a new algorithm for phase imaging that simultaneously unwraps phase and removes noise. This method, Simultaneous Phase Unwrapping and Denoising (SPUD), is faster and more accurate than existing techniques.

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Area of Science:

Image Processing
Signal Processing
Computational Imaging

Background:

Phase unwrapping is crucial for many imaging techniques.
Existing denoising methods increase computational complexity and execution time.
There is a need for efficient, simultaneous phase unwrapping and denoising.

Purpose of the Study:

To introduce a novel noniterative algorithm for simultaneous phase unwrapping and denoising (SPUD).
To evaluate SPUD's performance in terms of accuracy and speed compared to existing methods.
To demonstrate SPUD's applicability to real-world phase imaging data.

Main Methods:

The proposed method, SPUD, uses a least squares discrete cosine transform (DCT) solution.
A sparsity constraint is applied to the DCT coefficients of the unwrapped phase.
The algorithm is noniterative, enabling faster computation.

Main Results:

SPUD achieves accurate phase unwrapping and restoration under various noise levels.
The method outperforms the 2D windowed Fourier transform filter in phase error and execution time.
SPUD successfully processed synthetic aperture radar data, removing phase dislocations.

Conclusions:

SPUD offers an efficient and accurate solution for phase unwrapping and denoising.
The algorithm is suitable for processing real-world phase imaging data.
An accessible implementation of SPUD is available via Code Ocean.