Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images
In this study, we employed traditional methods and deep learning models to improve resolution and quality of low-resolution AFM images made under standard ambient scanning. Both traditional methods and deep learning models were benchmarked and quantified regarding fidelity, quality, and a survey tak...
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| Format: | Article |
| Language: | English |
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Beilstein-Institut
2025-07-01
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| Series: | Beilstein Journal of Nanotechnology |
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| Online Access: | https://doi.org/10.3762/bjnano.16.83 |
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| author | Samuel Gelman Irit Rosenhek-Goldian Nir Kampf Marek Patočka Maricarmen Rios Marcos Penedo Georg Fantner Amir Beker Sidney R. Cohen Ido Azuri |
| author_facet | Samuel Gelman Irit Rosenhek-Goldian Nir Kampf Marek Patočka Maricarmen Rios Marcos Penedo Georg Fantner Amir Beker Sidney R. Cohen Ido Azuri |
| author_sort | Samuel Gelman |
| collection | DOAJ |
| description | In this study, we employed traditional methods and deep learning models to improve resolution and quality of low-resolution AFM images made under standard ambient scanning. Both traditional methods and deep learning models were benchmarked and quantified regarding fidelity, quality, and a survey taken by AFM experts. The deep learning models outperform the traditional methods and yield better results. Additionally, some common AFM artifacts, such as streaking, are present in the ground truth high-resolution images. These artifacts are partially attenuated by the traditional methods but are completely eliminated by the deep learning models. This work shows deep learning models to be superior for super-resolution tasks and enables significant reduction in AFM measurement time, whereby low-pixel-resolution AFM images are enhanced in both resolution and fidelity through deep learning. |
| format | Article |
| id | doaj-art-bc7a212a7ac7483cafc3ae87368d09ea |
| institution | DOAJ |
| issn | 2190-4286 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Beilstein-Institut |
| record_format | Article |
| series | Beilstein Journal of Nanotechnology |
| spelling | doaj-art-bc7a212a7ac7483cafc3ae87368d09ea2025-08-20T03:12:35ZengBeilstein-InstitutBeilstein Journal of Nanotechnology2190-42862025-07-011611129114010.3762/bjnano.16.832190-4286-16-83Deep learning for enhancement of low-resolution and noisy scanning probe microscopy imagesSamuel Gelman0Irit Rosenhek-Goldian1Nir Kampf2Marek Patočka3Maricarmen Rios4Marcos Penedo5Georg Fantner6Amir Beker7Sidney R. Cohen8Ido Azuri9Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, 7610001, Israel Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel École Polytechnique Fédérale de Lausanne, Laboratory for Bio- and Nano-Instrumentation, CH1015 Lausanne, Switzerland École Polytechnique Fédérale de Lausanne, Laboratory for Bio- and Nano-Instrumentation, CH1015 Lausanne, Switzerland Bina, Weizmann Institute of Science, Rehovot, 7610001, Israel Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, 7610001, Israel In this study, we employed traditional methods and deep learning models to improve resolution and quality of low-resolution AFM images made under standard ambient scanning. Both traditional methods and deep learning models were benchmarked and quantified regarding fidelity, quality, and a survey taken by AFM experts. The deep learning models outperform the traditional methods and yield better results. Additionally, some common AFM artifacts, such as streaking, are present in the ground truth high-resolution images. These artifacts are partially attenuated by the traditional methods but are completely eliminated by the deep learning models. This work shows deep learning models to be superior for super-resolution tasks and enables significant reduction in AFM measurement time, whereby low-pixel-resolution AFM images are enhanced in both resolution and fidelity through deep learning.https://doi.org/10.3762/bjnano.16.83atomic force microscopydeep learningfast scanninglow resolutionsuper resolution |
| spellingShingle | Samuel Gelman Irit Rosenhek-Goldian Nir Kampf Marek Patočka Maricarmen Rios Marcos Penedo Georg Fantner Amir Beker Sidney R. Cohen Ido Azuri Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images Beilstein Journal of Nanotechnology atomic force microscopy deep learning fast scanning low resolution super resolution |
| title | Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images |
| title_full | Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images |
| title_fullStr | Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images |
| title_full_unstemmed | Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images |
| title_short | Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images |
| title_sort | deep learning for enhancement of low resolution and noisy scanning probe microscopy images |
| topic | atomic force microscopy deep learning fast scanning low resolution super resolution |
| url | https://doi.org/10.3762/bjnano.16.83 |
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