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Image restoration in frequency space using complex-valued CNNs

Z.H. Shah, M. Müller, W. Hübner, H. Ortkrass, B. Hammer, T. Huser, W. Schenck, Frontiers in Artificial Intelligence 7 (2024).

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Artikel | Veröffentlicht | Englisch
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Shah, Zafran HussainFH Bielefeld; Müller, Marcel; Hübner, Wolfgang; Ortkrass, Henning; Hammer, Barbara; Huser, Thomas; Schenck, WolframFH Bielefeld
Abstract
Real-valued convolutional neural networks (RV-CNNs) in the spatial domain have outperformed classical approaches in many image restoration tasks such as image denoising and super-resolution. Fourier analysis of the results produced by these spatial domain models reveals the limitations of these models in properly processing the full frequency spectrum. This lack of complete spectral information can result in missing textural and structural elements. To address this limitation, we explore the potential of complex-valued convolutional neural networks (CV-CNNs) for image restoration tasks. CV-CNNs have shown remarkable performance in tasks such as image classification and segmentation. However, CV-CNNs for image restoration problems in the frequency domain have not been fully investigated to address the aforementioned issues. Here, we propose several novel CV-CNN-based models equipped with complex-valued attention gates for image denoising and super-resolution in the frequency domains. We also show that our CV-CNN-based models outperform their real-valued counterparts for denoising super-resolution structured illumination microscopy (SR-SIM) and conventional image datasets. Furthermore, the experimental results show that our proposed CV-CNN-based models preserve the frequency spectrum better than their real-valued counterparts in the denoising task. Based on these findings, we conclude that CV-CNN-based methods provide a plausible and beneficial deep learning approach for image restoration in the frequency domain.
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Zeitschriftentitel
Frontiers in Artificial Intelligence
Band
7
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Shah, Zafran Hussain ; Müller, Marcel ; Hübner, Wolfgang ; Ortkrass, Henning ; Hammer, Barbara ; Huser, Thomas ; Schenck, Wolfram: Image restoration in frequency space using complex-valued CNNs. In: Frontiers in Artificial Intelligence Bd. 7, Frontiers Media SA (2024)
Shah ZH, Müller M, Hübner W, et al. Image restoration in frequency space using complex-valued CNNs. Frontiers in Artificial Intelligence. 2024;7. doi:10.3389/frai.2024.1353873
Shah, Z. H., Müller, M., Hübner, W., Ortkrass, H., Hammer, B., Huser, T., & Schenck, W. (2024). Image restoration in frequency space using complex-valued CNNs. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1353873
@article{Shah_Müller_Hübner_Ortkrass_Hammer_Huser_Schenck_2024, title={Image restoration in frequency space using complex-valued CNNs}, volume={7}, DOI={10.3389/frai.2024.1353873}, journal={Frontiers in Artificial Intelligence}, publisher={Frontiers Media SA}, author={Shah, Zafran Hussain and Müller, Marcel and Hübner, Wolfgang and Ortkrass, Henning and Hammer, Barbara and Huser, Thomas and Schenck, Wolfram}, year={2024} }
Shah, Zafran Hussain, Marcel Müller, Wolfgang Hübner, Henning Ortkrass, Barbara Hammer, Thomas Huser, and Wolfram Schenck. “Image Restoration in Frequency Space Using Complex-Valued CNNs.” Frontiers in Artificial Intelligence 7 (2024). https://doi.org/10.3389/frai.2024.1353873.
Z. H. Shah et al., “Image restoration in frequency space using complex-valued CNNs,” Frontiers in Artificial Intelligence, vol. 7, 2024.
Shah, Zafran Hussain, et al. “Image Restoration in Frequency Space Using Complex-Valued CNNs.” Frontiers in Artificial Intelligence, vol. 7, Frontiers Media SA, 2024, doi:10.3389/frai.2024.1353873.

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