Lensless hyperspectral phase imaging in a self-reference setup based on Fourier transform spectroscopy and noise...

Area of Science:

• Optical Physics
• Image Processing
• Spectroscopy

Background:

• Hyperspectral imaging requires accurate phase information for detailed analysis.
• Existing phase retrieval methods struggle with noisy intensity observations.
• Fellgett's disadvantage in Fourier transform spectroscopy amplifies noise, complicating phase reconstruction.

Purpose of the Study:

• To develop a novel phase retrieval algorithm for broadband hyperspectral phase imaging.
• To overcome limitations of existing methods in handling noisy data.
• To reconstruct both the phase and depth profile of objects.

Main Methods:

• Utilizing Fourier transform spectroscopy in a self-referencing optical setup.
• Implementing a sparse wavefront noise filtering technique within the algorithm.
• Validating the algorithm through simulations and physical experiments with transparent objects.

Main Results:

• The algorithm successfully reconstructs the phase distribution of investigated objects.
• Noise amplification, a known issue (Fellgett's disadvantage), is significantly mitigated.
• Precise phase imaging and accurate object depth (profile) reconstruction were demonstrated.

Conclusions:

• The proposed algorithm offers a robust solution for hyperspectral phase imaging from noisy data.
• It enhances phase reconstruction accuracy and enables detailed object profiling.
• The method shows promise for applications requiring high-resolution imaging of transparent materials.