Advanced Imaging, Volume. 1, Issue 2, 022001(2024)
Review of polarimetric image denoising Author Presentation , Editors' Pick
[16] X. Fan, B. Lin, Z. Guo. Infrared polarization-empowered full-time road detection via lightweight multi-pathway collaborative 2D/3D convolutional networks. IEEE Trans. Intell. Transp. Syst., 12762(2024).
[37] A. Abubakar, A. Bermak. An adaptive denoising algorithm for speckle noise in DoFP polarization images, 225(2021).
[45] N. Li et al. Joint denoising-demosaicking network for long-wave infrared division-of-focal-plane polarization images with mixed noise level estimation. IEEE Trans. Image Process., 5961(2023).
[46] Z. Li et al. Polarized color image denoising, 9873(2023).
[51] V. Deschaintre, Y. Lin, A. Ghosh. Deep polarization imaging for 3D shape and SVBRDF acquisition, 15567(2021).
[57] S. Qiu et al. Linear polarization demosaicking for monochrome and colour polarization focal plane arrays. Computer Graphics Forum, 40, 77(2021).
[58] D. H. Goldstein. Polarized Light(2017).
[59] K. Wei et al. A physics-based noise formation model for extreme low-light raw denoising, 2758(2020).
[61] R. L. Baer. A model for dark current characterization and simulation. Sensors, Cameras, and Systems for Scientific/Industrial Applications VII, 6068, 37(2006).
[62] A. K. Boyat, B. K. Joshi. A review paper: noise models in digital image processing(2015).
[64] C. Zhou et al. Polarization-aware low-light image enhancement, 3742(2023).
[67] S. Lefkimmiatis. Non-local color image denoising with convolutional neural networks, 3587(2017).
[69] A. Buades, B. Coll, J.-M. Morel. A non-local algorithm for image denoising, 60(2005).
[74] K. Dabov et al. BM3D image denoising with shape-adaptive principal component analysis(2009).
[81] S. Ioffe, C. Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift, 448(2015).
[82] V. Nair, G. E. Hinton. Rectified linear units improve restricted Boltzmann machines, 807(2010).
[84] K. He et al. Deep residual learning for image recognition, 770(2016).
[85] G. Huang et al. Densely connected convolutional networks, 4700(2017).
[86] Y. Zhang et al. Residual dense network for image super-resolution, 2472(2018).
[88] B. Park, S. Yu, J. Jeong. Densely connected hierarchical network for image denoising, 2104(2019).
[89] Q. Zhang et al. A parallel and serial denoising network. Expert Syst. Appl., 231, 120628(2023).
[91] C. Tian et al. Heterogeneous window transformer for image denoising(2024).
[92] J. Lehtinen et al. Noise2noise: learning image restoration without clean data, 2965(2018).
[100] K. Dabov et al. Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space(2007).
[102] J. Redmon et al. You only look once: unified, real-time object detection, 779(2016).
[104] K. Zhang et al. Learning deep CNN denoiser prior for image restoration, 3929(2017).
[106] Y. Ba et al. Deep shape from polarization. Computer Vision–ECCV 2020: 16th European Conference, 554(2020).
[107] C. Zhou et al. Learning to dehaze with polarization. Advances in Neural Information Processing Systems, 34, 11487(2021).
[108] D. Li et al. High-performance polarization remote sensing with the modified u-net based deep-learning network. IEEE Trans. Geosci. Remote Sens., 60, 1(2022).
[109] J. Hu, L. Shen, G. Sun. Squeeze-and-excitation networks, 7132(2018).
[110] Y. Kim et al. Transfer learning from synthetic to real-noise denoising with adaptive instance normalization, 3482(2020).
[111] C. Tan et al. A survey on deep transfer learning, 270(2018).
[112] K. Sohn et al. Unsupervised domain adaptation for face recognition in unlabeled videos, 3210(2017).
[113] M. Long et al. Deep transfer learning with joint adaptation networks, 2208(2017).
[115] T. Plotz, S. Roth. Benchmarking denoising algorithms with real photographs, 1586(2017).
[117] N. Moran et al. Noisier2noise: Learning to denoise from unpaired noisy data, 12061(2020).
[118] A. Krull, T. O. Buchholz, F. Jug. Noise2Void-learning denoising from single noisy images, 2129(2019).
[119] Z. Wang et al. Blind2unblind: self-supervised image denoising with visible blind spots, 2017(2022).
[122] S. Qiu et al. Polarization demosaicking for monochrome and color polarization focal plane arrays. International Symposium on Vision, Modeling and Visualization(2019).
[125] A. Vaswani et al. Attention is all you need(2017).
[128] Z. Wang et al. Uformer: a general u-shaped transformer for image restoration, 17683(2022).
[129] S. W. Zamir et al. Restormer: efficient transformer for high-resolution image restoration, 5728(2022).
[132] D. B. Chenault et al. Metrics for comparison of polarimetric and thermal target to background contrast(2018).
[133] J. L. Pezzaniti et al. Detection of obscured targets with IR polarimetric imaging. Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX, 347(2014).
[144] O. Ronneberger, P. Fischer, T. Brox. U-net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, 234(2015).
[146] S. Woo et al. CBAM: convolutional block attention module, 3(2018).
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Hedong Liu, Xiaobo Li, Zihan Wang, Yizhao Huang, Jingsheng Zhai, Haofeng Hu, "Review of polarimetric image denoising," Adv. Imaging 1, 022001 (2024)
Category: Review Article
Received: Apr. 15, 2024
Accepted: Sep. 4, 2024
Published Online: Oct. 11, 2024
The Author Email: Haofeng Hu (haofeng_hu@tju.edu.cn)