Super-resolution Convolutional Dictionary Learning

Convolutional sparse coding (CSC) and convolutional dictionary learning (CDL) for off-the-grid events. This code is for the manuscript

Song, A., Flores, F., and Ba D., Convolutional Dictionary Learning with Grid Refinement, IEEE Transaction on Signal Processing, 2020

• 1-D example for spike sorting application is available.
• 2-D example for Single-molecule-localization-microscopy (SMLM) will be made available soon.

Please email Andrew Song ([email protected]) for any questions/suggestions

Getting started

To get started, clone the repository and run

pip install -r requirements.txt

To run CDL without interpolation on spikesorting application run the following,

cd src/run_experiments
python run_experiments.py train --folder_name=spikesorting_no_interp

If you want to run CDL with interpolation, change "spikesorting_no_interp" to "spikesorting_interp".

To predict with the learned dictionary, run the following (change the folder name for interpolated dictionary as above),

cd src/run_experiments
python run_experiments.py predict --folder_name=spikesorting_no_interp

To generate the error curve (along with pre-computed baseline error curves), run (Depending on the platform, you might have to do display_spikesorting_errorcurve with underscore, not '-')

python extract_results.py display-spikesorting-errorcurve

This command will generate the error curve and save it.