JoDeFu

Implementation in MATLAB of the multiresolution compressed acquisition (MRCA) image formation and joint demosaicing and fusion (JoDeFu) image reconstruction method.

Description

The code is able to:

• model an optical acquisition of the MRCA, a device based on color filter arrays (CFAs) and/or multiresolution sensors;
• reconstruct an image datacube from their acquisition with the JoDeFu, a Bayesian inversion algorithm;
• compare the results of the estimated products with respect to classical demosaicing and sharpening algorithms.

An example of the expected results is given by the images below:

Reference	MRCA acquisition
Classic v2 reconstruction	JoDeFu v1 reconstruction

Getting started

Dependencies

The script requires the following MATLAB toolboxes:

• Signal Processing Toolbox
• Image Processing Toolbox
• Wavelet Toolbox

The provided scripts were tested on a licensed MATLAB 2018b version on a machine running Window 10.

Demo scripts

• Fully reproducible run (Warning: May take several hours.): matlab -nodisplay code/main/main.m

The repository contains demo scripts for the experiments provided in the associated article, which, for the user convenience, are all located in the src\main folder and the results are saved in the results folder. The results obtained running these algorithms fully match the ones given in the associated article. Specifically, the following tests are available:

• Image formation: Scripts which test the quality of image reconstructed starting from acquisitions modeled through a variety of image formation methods:
  • demo_formation_mrca.m: reconstructed through the JoDeFu v1 algorithm;
  • demo_formation_classic.m: reconstructed with classic noniterative algorithms;
  • demo_formation_cassi.m: reconstructed from CASSI acquisitions both with the CASSI decoder and JoDeFu v1;
  • demo_formation_software.m: obtained with software compression encoders/decoders.
• Image reconstruction: Scripts testing various reconstruction algorithm processing an MRCA acquisition:
  • demo_reconstruction_jodefu.m: reconstructed through JoDeFu v1 and v2.
  • demo_reconstruction_classic.m: reconstructed through cascaded classic demosaicing and sharpening algorithms;
• Setting of parameters: Scripts testing the settings of the JoDeFu algorithm:
  • demo_parameters.m: by varying the regularization parameter, the metric function norm, the total variation linear operator and the blurring diameter.

How to use

The MRCA model simulated acquisition and JoDeFu algorithm with validation can be run through code/jodefu/wrapper_compressed_acquisition.m with the following code snippet:

[I_out, I_acq, mask_out, MR] = wrapper_compressed_acquisition('im', im,...
    'ratio', ratio, 'mask', mask, 'test', test, 'inv', inv, 'radius', d_b,...
    'lambda', lambda, 'iter', iter, 'preproc', preproc, 'output', output);

where:

• im: is an image tag string, in the format 'place_cutM_N' (e.g. 'Washington_cut256_4'):
  • place: is the location label (Washington, Janeiro, Beijing, Hobart, SanFrancisco, Stockholm);
  • M: is the image side size (256 or 512);
  • N: is the number of channels (3, 4, or 8 if available);
• ratio is the integer scale ratio between the PAN and MS (e.g., 2 or 4)
• mask is a string label for the mask used by the MRCA framework:
  • 'Bayer': for a Bayer mask;
  • 'BinaryTreeU': for an uniform binary tree mask;
  • 'CASSI': for a CASSI encoder;
• test is a label string to describe the MRCA framework:
  • 'default': for the full MRCA;
  • 'nomask': for the multiresolution sensing;
  • 'msonly': for the mosaicing;
• inv is a struct with information about the image formation:
  • {'TV_c', 'norm_l221','none'} for the JoDeFu v1;
  • {'TV_u', 'norm_S1l1','none'} for the JoDeFu v2 with upwind total variation (UTV);
  • {'TV_s2', 'norm_S1l1','none'} for the JoDeFu v2 with Shannon total variation (STV) with magnification factor 2;
  • {'none', 'norm_l111','CAS_sym8'} for the CASSI LASSO decoder;
• d_b is a float above 1, representing the blurring diameter (e.g. 1 or 1.4);
• lambda is the normalized regularization parameter (e.g. 0.001);
• iter is the number of iterations of the JoDeFu algorithm (e.g. 250);
• preproc is a string that describes the method to evaluate the coefficients for the spatial degradation:
  • 'regravg': all coefficients are identical (after the MS is histogram matched to the PAN);
  • 'hism': the coefficients are evaluated with linear regression;
• output: is a string describing the subfolder to save results under data\output (e.g. 'results_test')

The script returns:

• I_out: The reconstructed estimated product;
• I_acq: The simulated acquisition;
• mask_out: The mask generated for image formation;
• MR: A struct containing the verification results.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

Some included code snippets are inspired or modified from:

• Laurent Condat's convex optimization toolbox
• Gemine Vivone's open remote sensing pansharpening toolbox
• BlindFuse
• Residual interpolation
• Alternating projections
• Multiscale gradients-based color filter array interpolation
• Shannon total variation
• Scott Sarra's radial basis function toolbox

The provided sample datasets are courtesy of Maxar:

• Maxar sample products

Citation

If you use this code, please cite:

@article{picone23jodefu,
  author    = {Picone, Daniele and Dalla Mura, Mauro and Condat, Laurent},
  journal   = {IEEE Transactions on Computational Imaging},
  title     = {Joint demosaicing and fusion of multiresolution coded acquisitions: {A} unified image formation and reconstruction method},
  year      = {2023},
  month     = mar,
  pages     = {335--349},
  volume    = {9},
  doi       = {10.1109/tci.2023.3261503},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}