WISP: Workframe for Interferogram Signal Phase-Unwrapping

WISP: Workframe for Interferogram Signal Phase-Unwrapping

This paper proposes an iterative framework WISP (Workframe for Interferogram Signal Phase-unwrapping for phase reconstruction from interferograms of complex phase objects. The framework processes each interferogram through the sequential stages: dark fringe tracing, isophase distribution, local grad...

Full description

Saved in:
Bibliographic Details
Main Authors: Timofey F. Khirianov, Aleksandra I. Khirianova, Egor V. Parkevich, Ilya Makarov
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Phase unwrapping
fringe tracing
optical systems
computational imaging
adaptive optics
Online Access:https://ieeexplore.ieee.org/document/11091281/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes an iterative framework WISP (Workframe for Interferogram Signal Phase-unwrapping for phase reconstruction from interferograms of complex phase objects. The framework processes each interferogram through the sequential stages: dark fringe tracing, isophase distribution, local gradient direction estimation, anisotropic (local direction dependent) diffusion smoothing, phase unwrapping, and convergence testing. Iterations continue until the difference between the reconstructed and experimental phase distributions reaches an asymptotic minimum. A key contribution is the proposed loss function for isophase fitting, which directly optimizes curve quality and enhances reconstruction accuracy. Experimental results confirm the algorithm’s highest precision. A study of the framework’s resistance to noise was conducted, showing high stability even in the case of noise with an amplitude half the amplitude of the image brightness. Comparative analysis against established baselines reveals that the WISP consistently outperforms alternative approaches in accurately unwrapping the phase, particularly under high noise conditions. Evaluated using RMSD metrics, WISP achieves the lowest reconstruction errors, reducing them by 39.7% compared to the next best method (Deep Convolutional Neural Network), highlighting its superior robustness and accuracy.
ISSN:2169-3536

Similar Items