Impementation of state-of-art Short and sparse blind deconvolution in Python following this paper:
'Short-and-Sparse Deconvolution — A Geometric Approach' by: Lau, Yenson and Qu, Qing and Kuo, Han-Wen and Zhou, Pengcheng and Zhang, Yuqian and Wright, John
iADM and iADM with homotopy on 1D vectors on : iADM.py
iADM and iADM with homotopy and iADM with reweighting on 2D vectors or images on : iADM2D.py
Jupyter notebook of testing recovery of 1D vectors recovery : test.ipynb
Jupyter notebook of recovering calcium image recovery : Deconvolve2D.ipynb
-The microscopic image of high temperature superconductor polycrystalline Deconvoution:
-Natural image deblurring (if using image gradient as they are sparse):
-Convolutional Dictionary learning
(https://deconvlab.github.io/)
@article{lau2019short, title={Short-and-Sparse Deconvolution — A Geometric Approach}, author={Lau, Yenson and Qu, Qing and Kuo, Han-Wen and Zhou, Pengcheng and Zhang, Yuqian and Wright, John}, journal={Preprint}, year={2020} } }
@article{kuo2019geometry, title={Geometry and symmetry in short-and-sparse deconvolution}, author={Kuo, Han-Wen and Zhang, Yuqian and Lau, Yenson and Wright, John}, journal={arXiv preprint arXiv:1901.00256}, year={2019} } }