Integrated diffractive full-Stokes spectro-polarimetric imaging.

Area of Science:

• Optics and Photonics
• Image Processing
• Materials Science

Background:

• Spectro-polarimetric imaging offers rich optical information but faces challenges with system size, complexity, and cost.
• Existing systems often require specialized polarization optics, increasing design hurdles.

Purpose of the Study:

• To propose and demonstrate an integrated diffractive full-Stokes spectro-polarimetric imaging framework.
• To overcome the limitations of conventional spectro-polarimetric imaging systems.

Main Methods:

• Developed an end-to-end designed diffractive polarization spectral element (DPSE) for phase and polarization modulation.
• Utilized a neural network (SPMSA-Net) for reconstructing spectro-polarimetric data cubes from modulated images.
• Integrated DPSE and SPMSA-Net into a compact imaging framework.

Main Results:

• Achieved an average improvement of 0.78 dB in PSNR and 0.012 in SSIM compared to state-of-the-art methods.
• Demonstrated simultaneous capture of spectral information (400-700 nm, 10 nm resolution) and full-Stokes parameters.
• Attained high spatial resolution (2252x2252 pixels) and spectral imaging fidelity over 98.9%.

Conclusions:

• The proposed diffractive imaging framework enables high-performance spectro-polarimetric imaging with a compact architecture.
• The system offers precise polarization characterization and high-fidelity spectral imaging capabilities.
• This approach significantly reduces the physical footprint and complexity of spectro-polarimetric systems.