Optimal compressive multiphoton imaging at depth using single-pixel detection.

Area of Science:

• Optics and Photonics
• Biomedical Imaging
• Signal Processing

Background:

• Compressive sensing (CS) allows imaging with fewer measurements than traditional methods, surpassing the Nyquist criterion.
• High-resolution microscopy techniques like multiphoton imaging are often limited by diffraction and scattering.
• Conventional measurement bases in CS are sensitive to these optical aberrations.

Purpose of the Study:

• To investigate the random Morlet basis as an optimal measurement set for compressive multiphoton imaging.
• To address the limitations of diffraction and scattering in high-resolution microscopy.
• To enable rapid imaging with reduced photodamage.

Main Methods:

• Exploration of the random Morlet basis for its space-frequency uncertainty minimization properties.
• Implementation of compressive multiphoton microscopy using a single-pixel detector.
• Application of the random Morlet basis in wide-field multiphoton microscopy.

Main Results:

• The random Morlet basis demonstrated effectiveness in minimizing space-frequency uncertainty for imaging.
• The developed method allowed for wide-field multiphoton imaging through turbid media.
• Imaging was achieved without the need for aberration correction techniques.

Conclusions:

• The random Morlet basis offers an advantageous approach for compressive multiphoton imaging.
• This method facilitates rapid image acquisition and reduces photodamage.
• The technique shows potential for imaging in challenging scattering environments.