Poisson2Poisson-Sparse: Unsupervised Poisson noise image denoising based on sparse modeling
In fields such as low-light photography, astronomical imaging, and low-dose computed tomography scanning, Poisson noise severely degrades image quality due to extremely low photon counts (averaging below one) and their Poisson-distributed statistical characteristics. A recent self-supervised Poisson...
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| Veröffentlicht in: | Signal processing Jg. 230; S. 109870 |
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01.05.2025
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| Abstract | In fields such as low-light photography, astronomical imaging, and low-dose computed tomography scanning, Poisson noise severely degrades image quality due to extremely low photon counts (averaging below one) and their Poisson-distributed statistical characteristics. A recent self-supervised Poisson denoising method uses only a single noisy image to improve image quality. However, it struggles under high Poisson noise due to its denoising model based on Gaussian distribution and suffers from long inference times. To address these issues, we propose an unsupervised Poisson denoising method based on sparse representation. Specifically, we first establish a more accurate sparse representation model based on Poisson distribution to enhance denoising performance. Given the difficulty of solving this model directly, we develop an iterative optimization algorithm using convolutional sparse coding and the alternating direction method of multipliers. Inspired by the unfolding technique, we further reduce computational cost by unfolding the iterative process into a finite-cycle learning network. To overcome the reliance on paired datasets and accelerate inference times, we employ a Poisson loss function, a Neighbor2Neighbor training strategy, and incorporate total variation loss, which together enable unsupervised learning. Experimental results demonstrate that our proposed method significantly outperforms existing unsupervised Poisson denoising methods and achieves high computational efficiency.
•The Poisson fidelity term surpasses the Gaussian term in low-light Poisson denoising.•Effectively unfold the alternating direction method of multipliers as a network.•Convolutional sparse coding serves as a byproduct of the denoising network.•The optimal unsupervised unfolding method for Poisson denoising in low light.•TV loss effectively suppresses artifacts in Poisson unsupervised denoising. |
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| AbstractList | In fields such as low-light photography, astronomical imaging, and low-dose computed tomography scanning, Poisson noise severely degrades image quality due to extremely low photon counts (averaging below one) and their Poisson-distributed statistical characteristics. A recent self-supervised Poisson denoising method uses only a single noisy image to improve image quality. However, it struggles under high Poisson noise due to its denoising model based on Gaussian distribution and suffers from long inference times. To address these issues, we propose an unsupervised Poisson denoising method based on sparse representation. Specifically, we first establish a more accurate sparse representation model based on Poisson distribution to enhance denoising performance. Given the difficulty of solving this model directly, we develop an iterative optimization algorithm using convolutional sparse coding and the alternating direction method of multipliers. Inspired by the unfolding technique, we further reduce computational cost by unfolding the iterative process into a finite-cycle learning network. To overcome the reliance on paired datasets and accelerate inference times, we employ a Poisson loss function, a Neighbor2Neighbor training strategy, and incorporate total variation loss, which together enable unsupervised learning. Experimental results demonstrate that our proposed method significantly outperforms existing unsupervised Poisson denoising methods and achieves high computational efficiency.
•The Poisson fidelity term surpasses the Gaussian term in low-light Poisson denoising.•Effectively unfold the alternating direction method of multipliers as a network.•Convolutional sparse coding serves as a byproduct of the denoising network.•The optimal unsupervised unfolding method for Poisson denoising in low light.•TV loss effectively suppresses artifacts in Poisson unsupervised denoising. |
| ArticleNumber | 109870 |
| Author | Wei, Jiuzhe Wang, Shengbiao Yang, Shihua Xiao, Lingzhi Zhang, Jun |
| Author_xml | – sequence: 1 givenname: Lingzhi orcidid: 0009-0007-0005-3611 surname: Xiao fullname: Xiao, Lingzhi email: xiaolingzhi2000@njust.edu.cn organization: School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China – sequence: 2 givenname: Shengbiao surname: Wang fullname: Wang, Shengbiao organization: School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China – sequence: 3 givenname: Jun surname: Zhang fullname: Zhang, Jun email: phil_zj@njust.edu.cn organization: School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China – sequence: 4 givenname: Jiuzhe surname: Wei fullname: Wei, Jiuzhe organization: Beijing Institute of Space Mechanics and Electricity, Beijing, 100076, China – sequence: 5 givenname: Shihua surname: Yang fullname: Yang, Shihua organization: Beijing Institute of Space Mechanics and Electricity, Beijing, 100076, China |
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| Cites_doi | 10.1002/cpa.20042 10.1109/CVPRW.2019.00242 10.23919/ICCAS55662.2022.10003847 10.1109/TIP.2018.2859044 10.1109/CVPR.2015.7299163 10.1088/2399-6528/ac0eca 10.1109/ICIP.2010.5653394 10.2307/2332343 10.1109/TIP.2010.2056693 10.1109/ICIP49359.2023.10222230 10.1109/ICASSP.2018.8462313 10.1109/ICASSP43922.2022.9746870 10.1198/106186004X2697 10.3934/ipi.2021003 10.1109/TIP.2021.3090531 10.1109/TIP.2021.3087943 10.1109/TIP.2014.2362057 10.1007/s10851-013-0435-6 10.1109/TIP.2022.3176533 |
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| Keywords | Unfolding Poisson noise Convolutional sparse coding Unsupervised learning Image denoising |
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| Title | Poisson2Poisson-Sparse: Unsupervised Poisson noise image denoising based on sparse modeling |
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