End-to-end deep unfolding network for DoFP polarization image reconstruction

Research context

Research on electrical energy conversion, storage and generation dates back to the nineteenth century, but only in recent years have scientists begun to investigate the impact of electron spin on these processes. The ability to control and manipulate this intrinsically quantum property of matter opens new approaches to addressing energy science challenges. The chiral-induced spin selectivity (CISS) effect is central to this effort, as it enables control over the transport and generation of both pure spin currents and spin-polarized charge currents. In this Review, we first introduce design strategies for implementing CISS in materials and then describe examples of how CISS has been used to improve electrocatalysis and spintronics. We conclude with a forward-looking perspective on the next steps for leveraging CISS in energy science.

Keywords: Polarization (electrochemistry), Iterative reconstruction, Cardinal point, Image resolution, Linear polarization, Radial polarization, Polarimetry, Stokes parameters, Image plane, Optics, Computer science, Artificial intelligence, Physics, Computer vision, Image processing, Inference, Object detection, Multiplexing, Image restoration, Deep learning, Superresolution, Speckle noise, Reconstruction algorithm, Spatial frequency

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Cite / 引用

Xin Liu, Zhiqiu Yan, Zhenyuan Huang, Pan Wang, Siju Peng, Yuanxing Zhao, Li Wang, Qilong Wang. End-to-end deep unfolding network for DoFP polarization image reconstruction. Optics Express (2025). https://doi.org/10.1364/oe.581615

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