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CHPDNet: a chromatic high-resolution polarization decoding network for snapshot polarization imaging
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DOI：10.1364/AO.585254.png)
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Abstract
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To address the issue of spatial resolution loss in existing polarization imaging systems, this paper proposes a snapshot chromatic high-resolution polarization imaging method based on the deep learning technology. Using the images captured by pupil-divided polarization imaging scheme as the input, chromatic high-resolution polarized images can be simultaneously retrieved by the proposed chromatic high-resolution polarization decoding network (CHPDNet). Experimental results show that the intensity (S0) image was reconstructed with high precision, exhibiting a relative error below 1.2%. Meanwhile, the degree of polarization (DoP) maps were also accurately recovered, with errors below 12% for the G and B channels and below 16% for the R channel, along with structurally complete AoP images. The proposed method may offer a new approach to the design of chromatic simultaneous polarization imaging methods. (c) 2026 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
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