This repository contains the Bayesian image reconstruction algorithm we used in our project: Bayesian Image Reconstruction from Cone Mosaic Signal. For details, visit the main Project Page.
Please also see the Methods section of our paper. For actual usage, the best way is to start with the instructions in the Project Page.
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├── coneMosaic
├── ConeResponse.m # Base Class, Wrapper of ISETBio, compute cone responses to natural images
├── ConeResponsePeripheral.m # Extend the base class to use peripheral cone mosaic and optics
├── PeripheralModel.m # Static function that returns optical model (PSF) at different visual eccentricity
├── ConeResponseCmosaic.m # Extend the base class to use the newly updated cone mosaic
├── imageEstimator # Deprecated; Regression-based method for image reconstruction
├── ...
├── patchEstimator # The main algorithm used in our paper
├── PatchEstimator.m # Base class for the estimator
├── GaussianPatch... # Implement the Gaussian image prior
├── SparsePatch... # Implement the sparse coding image prior
├── Poisson... # Implement the Poisson likelihood function
├── imageHelper # Some helper function for image processing
├── MarkovPrior.m # Gaussian prior for which we have control over its spatial and chromatic correlation
├── computeBasisPCA.m # PCA on dataset of natural images
├── whitening.m # Apply the whitening transformation to images
├── sampleImage.m # Crop image to a specific size
├── labDistance.m # The Spatial CIELAB loss function
├── ...
├── visualHelper # Some helper functions for visualization
├── ...
├── hyperspectral # Data loader and data curation with hyperspectral images
├── ...
├── fminlbfgs.m # MATLAB community implementation of limited memory BFGS.
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Please see here for the original source of fminlbfgs.
Feel free to contact me if you any questions or comments at
lingqiz at sas dot upenn dot edu