Instructions to use timofeiiz/lensless-computational-imaging with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use timofeiiz/lensless-computational-imaging with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timofeiiz/lensless-computational-imaging", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - image-restoration | |
| - lensless-imaging | |
| license: mit | |
| # Lensless Computational Imaging | |
| 10 models for reconstructing images from a lensless camera. | |
| Trained on [DigiCam-Mirflickr-MultiMask-10K](https://huggingface.co/datasets/bezzam/DigiCam-Mirflickr-MultiMask-10K). | |
| ## Models | |
| **Classical** | |
| | Subfolder | Description | | |
| |---|---| | |
| | `admm100` | ADMM, 100 iterations, fixed hyperparameters. | | |
| | `fista100` | FISTA, 100 iterations, fixed hyperparameters. | | |
| **Unrolled** | |
| | Subfolder | Description | | |
| |---|---| | |
| | `le_admm20` | ADMM, 20 unrolled steps with learnable per-step hyperparameters. | | |
| | `le_fista20` | FISTA, 20 unrolled steps with learnable per-step hyperparameters. | | |
| **Modular** | |
| | Subfolder | Description | | |
| |---|---| | |
| | `modular_le_admm5_prepost` | Pre-UNet β 5 ADMM steps β post-UNet. | | |
| | `modular_le_admm5_pre` | Pre-UNet β 5 ADMM steps. | | |
| | `modular_le_admm5_post` | 5 ADMM steps β post-UNet. | | |
| | `modular_le_fista5_prepost` | Pre-UNet β 5 FISTA steps β post-UNet. | | |
| **GAN super-resolution** | |
| | Subfolder | Description | | |
| |---|---| | |
| | `admm100_bsrgan` | ADMM-100 + pretrained BSRGAN x4. | | |
| | `admm100_bsrgan_finetune` | ADMM-100 + BSRGAN x4 fine-tuned on DigiCam. | | |
| ## Usage | |
| ```python | |
| from transformers import AutoModel | |
| model = AutoModel.from_pretrained( | |
| "timofeiiz/lensless-computational-imaging", | |
| subfolder="modular_le_admm5_prepost", | |
| trust_remote_code=True, | |
| ) | |
| recon = model(lensless, psf) | |
| ``` | |