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| Citation: |
Gigan S. Data-driven polarimetric approaches fuel computational imaging expansion. Opto-Electron Adv 7, 240158 (2024). doi: 10.29026/oea.2024.240158
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Data-driven polarimetric approaches fuel computational imaging expansion
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Abstract
Incorporating polarization in computer vision tasks provides new solutions to high-level analytics, in particular when coupled with machine learning frameworks such as convolutional neural networks (CNN). A recent review in Opto-Electronic Science reports on the developments in data-driven polarimetric imaging, including polarimetric descattering, 3D imaging, reflection removal, target detection and biomedical imaging. The review carefully analyzes these new trends with their advantages and disadvantages, and provides a general insight for future research and development. -
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References
[1] Yang K, Liu F, Liang SY et al. Data-driven polarimetric imaging: a review. Opto-Electron Sci 3, 230042 (2024). doi: 10.29026/oes.2024.230042 [2] Hu HF, Zhang YB, Li XB et al. Polarimetric underwater image recovery via deep learning. Opt Lasers Eng 133, 106152 (2020). doi: 10.1016/j.optlaseng.2020.106152 [3] Liu HD, Zhang YZ, Cheng ZZ et al. Attention-based neural network for polarimetric image denoising. Opt Lett 47, 2726–2729 (2022). doi: 10.1364/OL.458514 [4] Zhang JC, Shao JB, Luo HB et al. Learning a convolutional demosaicing network for microgrid polarimeter imagery. Opt Lett 43, 4534–4537 (2018). doi: 10.1364/OL.43.004534 [5] Wu XS, Zhang H, Hu XP et al. HDR reconstruction based on the polarization camera. IEEE Robot Autom Lett 5, 5113–5119 (2020). doi: 10.1109/LRA.2020.3005379 [6] Wieschollek P, Gallo O, Gu JW et al. Separating reflection and transmission images in the wild. In 15th European Conference on Computer Vision 90–105 (Springer, 2018); https://doi.org/10.1007/978-3-030-01261-8_6. [7] Hu HF, Lin Y, Li XB et al. IPLNet: a neural network for intensity-polarization imaging in low light. Opt Lett 45, 6162–6165 (2020). doi: 10.1364/OL.409673 [8] Ba YH, Gilbert AR, Wang F et al. Deep shape from polarization. In 16th European Conference on Computer Vision 554–571 (Springer, 2020); https://doi.org/10.1007/978-3-030-58586-0_33. [9] Kalra A, Taamazyan V, Rao SK et al. Deep polarization cues for transparent object segmentation. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition 8599–8608 (IEEE, 2020); https://doi.org/10.1109/CVPR42600.2020.00863. [10] Shen Y, Lin WF, Wang ZF et al. Rapid detection of camouflaged artificial target based on polarization imaging and deep learning. IEEE Photonics J 13, 7800309 (2021). [11] Li XP, Liao R, Zhou JL et al. Classification of morphologically similar algae and cyanobacteria using Mueller matrix imaging and convolutional neural networks. Appl Opt 56, 6520–6530 (2017). doi: 10.1364/AO.56.006520 [12] Wang YH, Louie DC, Cai JY et al. Deep learning enhances polarization speckle for in vivo skin cancer detection. Opt Laser Technol 140, 107006 (2021). doi: 10.1016/j.optlastec.2021.107006 -
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Gigan S. Data-driven polarimetric approaches fuel computational imaging expansion. Opto-Electron Adv 7, 240158 (2024). doi: 10.29026/oea.2024.240158
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Figure 1.
Applications of data-driven polarization imaging1.
