lensless-reconstruction

Lensless Reconstruction

PyTorch checkpoints for lensless image reconstruction on bezzam/DigiCam-Mirflickr-MultiMask-10K. The models reconstruct RGB scenes from coded-mask sensor measurements and PSFs.

Code and demo: daminovkamil/lensless-reconstruction

Checkpoints

Each .pth checkpoint stores its own Hydra config, so no separate config file is needed for inference. sample.zip contains a small custom-layout sample for the Colab/demo flow.

Metrics

All metrics are computed on the ROI of the DigiCam test split.

Usage

Clone the project repo and run inference with the hosted weights:

git clone https://github.com/daminovkamil/lensless-reconstruction
cd lensless-reconstruction
pip install -r requirements.txt

python inference.py \
  inferencer.from_pretrained=daminovkamil/lensless-reconstruction \
  inferencer.pretrained_filename=model_best.pth \
  datasets.test.data_dir=demo_sample

Use the FISTA checkpoint by changing only the filename:

python inference.py \
  inferencer.from_pretrained=daminovkamil/lensless-reconstruction \
  inferencer.pretrained_filename=fista_prepost5.pth \
  datasets.test.data_dir=demo_sample

Reconstructions are written to data/saved/reconstructions/<split>/<ImageID>.png.

Citation

This homework implementation follows the ADMM formulation from Monakhova et al. 2019 and the modular learned reconstruction setup from Bezzam et al. 2025.