Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy Image Reconstruction
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917780479016960 |
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| author | Pappas, Odysseas Mamou, Jonathan Basarab, Adrian Kouame, Denis Achim, Alin |
| author_facet | Pappas, Odysseas Mamou, Jonathan Basarab, Adrian Kouame, Denis Achim, Alin |
| contents | Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical properties of biological tissue at microscopy scale. Increased frequency QAM allows for finer resolution at the expense of increased acquisition times and data storage cost. Compressive sampling (CS) methods have been employed to produce QAM images from a reduced sample set, with recent state of the art utilising Approximate Message Passing (AMP) methods. In this paper we investigate the use of AMP-Net, a deep unfolded model for AMP, for the CS reconstruction of QAM parametric maps. Results indicate that AMP-Net can offer superior reconstruction performance even in its stock configuration trained on natural imagery (up to 63% in terms of PSNR), while avoiding the emergence of sampling pattern related artefacts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13634 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy Image Reconstruction Pappas, Odysseas Mamou, Jonathan Basarab, Adrian Kouame, Denis Achim, Alin Image and Video Processing Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical properties of biological tissue at microscopy scale. Increased frequency QAM allows for finer resolution at the expense of increased acquisition times and data storage cost. Compressive sampling (CS) methods have been employed to produce QAM images from a reduced sample set, with recent state of the art utilising Approximate Message Passing (AMP) methods. In this paper we investigate the use of AMP-Net, a deep unfolded model for AMP, for the CS reconstruction of QAM parametric maps. Results indicate that AMP-Net can offer superior reconstruction performance even in its stock configuration trained on natural imagery (up to 63% in terms of PSNR), while avoiding the emergence of sampling pattern related artefacts. |
| title | Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy Image Reconstruction |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2409.13634 |