| Autor: |
Pragyan Banerjee, Sibasish Mishra, Nitin Yadav, Krishna Agarwal, Frank Melandsø, Dilip K. Prasad, Anowarul Habib |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
AIP Advances, Vol 13, Iss 4, Pp 045225-045225-12 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2158-3226 |
| DOI: |
10.1063/5.0139034 |
| Popis: |
Scanning acoustic microscopy (SAM) is a non-ionizing and label-free imaging modality used to visualize the surface and internal structures of industrial objects and biological specimens. The image of the sample under investigation is created using high-frequency acoustic waves. The frequency of the excitation signals, the signal-to-noise ratio, and the pixel size all play a role in acoustic image resolution. We propose a deep learning-enabled image inpainting for acoustic microscopy in this paper. The method is based on training various generative adversarial networks (GANs) to inpaint holes in the original image and generate a 4× image from it. In this approach, five different types of GAN models are used: AOTGAN, DeepFillv2, Edge-Connect, DMFN, and Hypergraphs image inpainting. The trained model’s performance is assessed by calculating the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) between network-predicted and ground truth images. The Hypergraphs image inpainting model provided an average SSIM of 0.93 for 2× and up to 0.93 for the final 4×, respectively, and a PSNR of 32.33 for 2× and up to 32.20 for the final 4×. The developed SAM and GAN frameworks can be used in a variety of industrial applications, including bio-imaging. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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