Efficient multiscale phase unwrapping methodology with modulo wavelet transform.

Area of Science:

• Image processing
• Signal processing
• Computational imaging

Background:

• Phase unwrapping is crucial for many imaging techniques.
• Existing algorithms are often too slow for large datasets or real-time use.
• Computational time is a major bottleneck in phase unwrapping applications.

Purpose of the Study:

• To develop a computationally efficient phase unwrapping framework.
• To significantly reduce the computational load of phase unwrapping algorithms.
• To create a versatile framework applicable to various phase unwrapping methods.

Main Methods:

• A novel wavelet transform incorporating reversible modulo operators was developed.
• This transform reduces the number of coefficients requiring unwrapping.
• The framework is designed to be compatible with existing phase unwrapping algorithms.

Main Results:

• The proposed framework achieved speedup factors of up to 500.
• Significant computational gains were observed across various wrapped phase datasets.
• The method demonstrated practical applicability for large-scale and real-time scenarios.

Conclusions:

• The novel wavelet transform framework offers a substantial improvement in phase unwrapping efficiency.
• This approach makes advanced phase unwrapping feasible for previously impractical applications.
• Publicly available source code facilitates adoption and further research.