Datasets:
File size: 1,852 Bytes
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size_categories:
- 10K<n<100K
task_categories:
- image-to-image
dataset_info:
features:
- name: lensless
dtype: image
- name: lensed
dtype: image
splits:
- name: train
num_bytes: 5600452730.0
num_examples: 24000
- name: test
num_bytes: 230987060.0
num_examples: 999
download_size: 5873531182
dataset_size: 5831439790.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
tags:
- lensless
- computational-imaging
---
More accessible (6GB instead of 100GB) copy of: https://waller-lab.github.io/LenslessLearning/dataset.html
Original license: https://github.com/Waller-Lab/LenslessLearning/blob/master/LICENSE
This dataset was prepared with [this script](https://github.com/LCAV/LenslessPiCam/blob/main/scripts/data/upload_diffusercam_huggingface.py).
After cloning and installing [LenslessPiCam](https://github.com/LCAV/LenslessPiCam), ADMM reconstruction can be applied to the dataset with [this script](https://github.com/LCAV/LenslessPiCam/blob/main/scripts/recon/dataset.py) (handles dataset downloading from Hugging Face).
```bash
python scripts/recon/dataset.py
```
The models in [this collection](https://huggingface.co/collections/bezzam/diffusercam-mirflickr-65c05164df72cf99e5066658) use the [original DiffuserCam MirFlickr dataset](https://waller-lab.github.io/LenslessLearning/dataset.html) during training.
This dataset tries to replicate that version of the dataset (using NPY files during training).
One slight different is that we were required to subtract the mininum of value the numpy arrays so that they could be stored as viewable images.
For future training, it is recommended to use [this normalized version](https://huggingface.co/datasets/bezzam/DiffuserCam-Lensless-Mirflickr-Dataset-NORM) of the dataset. |