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Stokes' Law

Stokes' Law

Viscous forces, like friction, are intermolecular forces that resist the relative motion of molecules over each other. When a solid body moves through a liquid, viscous forces drag it in the opposite direction. The force's magnitude depends on the solid's shape and size, as well as its speed and the liquid's coefficient of viscosity, density and temperature.
The expression for the force on a solid spherical object in a fluid is called Stokes' law. Stokes' law is valid only...

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Multispectral polarization image demosaicing using redundant Stokes representation.

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Summary

This summary is machine-generated.

This study introduces a deep learning algorithm for multispectral polarization demosaicing (MPD-RS) that enhances image reconstruction. MPD-RS achieves superior performance by learning spatial, spectral, and polarization correlations for accurate multispectral polarization image (MSPI) recovery.

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Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Multispectral polarization filter arrays capture rich scene information.
Accurate reconstruction of multispectral polarization images (MSPIs) is crucial for various applications.
Existing demosaicing methods struggle with the complexity of polarization data.

Purpose of the Study:

To propose a novel deep-learning-based demosaicing algorithm, multispectral polarization demosaicing with redundant Stokes (MPD-RS).
To effectively learn correlations across spatial, spectral, and polarization domains for improved MSPI reconstruction.
To introduce a new Stokes representation that captures polarization redundancy.

Main Methods:

Developed MPD-RS algorithm utilizing a newly constructed dataset of MSPIs.
Employed a position-variant convolutional kernel for initial MSPI interpolation.
Introduced a novel Stokes representation decomposing data into four components, including polarization redundancy.
Processed intensity using a 3D convolutional network and other components with an attention-based network.

Main Results:

MPD-RS demonstrated superior performance in MSPI reconstruction compared to existing methods.
Achieved an average PSNR improvement of 3.873 dB over the Global Cross-Attention Network.
Showcased reduced mean squared error in Stokes parameters and high accuracy across diverse polarization levels.

Conclusions:

MPD-RS effectively reconstructs multispectral polarization images by leveraging deep learning.
The proposed algorithm offers significant improvements in PSNR and SSIM for MSPIs.
MPD-RS exhibits adaptability and maintains accuracy for images with varying polarization levels.