Data set for Noise reduction of coherent diffraction image data using deep learning method
| About | Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional background processing. In this data repository, sample codes, training dataset and learned model for deep learning method which was used to validate noise reduction techniques are provided. |
|---|---|
| URL | https://isspns-gitlab.issp.u-tokyo.ac.jp/t-ishikawa/SaclaDenoise |
| DOI | https://doi.org/10.48550/arXiv.2403.11992 |
| Authors | Takuto Ishikawa , Kazuyoshi Yoshimi , Takashi Kimura |
| License | CC-BY 4.0 |
| Category | Machine learning |
| References | T. Ishikawa et al. "Sub-photon accuracy noise reduction of single shot coherent diffraction pattern with atomic model trained autoencoder", arXiv:2403.11992 (2024). |
| Related software | Tensorflow, Keras |
| Contact ※Please replace __at__ with @. |
takuto-ishikawa__at__issp.u-tokyo.ac.jp, k-yoshimi__at__issp.u-tokyo.ac.jp,kimura__at__edm.t.u-tokyo.ac.jp |
| Note |