Matlab code of the PGDA approach described in
Benfenati A., Catozzi A., Ruggiero V., Neural Blind Deconvolution with Poisson Data, Inverse Problems, 39, 054003, link
The demo file Main.m sets the algorithm's parameters for solving the blind deconvolution problem as described in the paper. A brief description of the files is given.
- Main.m : Demo file. It runs the entire procedure
- DDIPP_UNET.m : Creates the UNET network used for the image restoration
- SIREN.m : Creates the SIREN network for the recostruction of the Point Spread Function (PSF)
- lagrangian_TotVar.m : Implements the loss function of the training
- TV_dlarrayPGDA.m : Implements the Total Variation functional
- NetInit.m : Initialization of the networks
- lossTrainNet.m : loss function for the initialization of the networks
- softThresh.m : Implenents the soft threshodling operator
- projectDF.m : Implemets the projection for the PSF (more documentation available here, under in SGP-dec.tgz repository
- psfGauss.m : Initializes a Gaussian PSF of given dimension and standard deviation
- loadData.m : Loads the data from a .mat file. Such file must contain the ground truths (obj and PSF), the blurred and noisy corrupted image (gn) and the parameter rho for the PSF
- onlineVisualization.m : Routine for online visualization of the running results
| Image | PSF | gn |
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Remark: the above images have been modified for visualization purposes.