Software
- Software available here, If you use this software, we request that you cite:
- S.Reynolds et al. ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data, (open access), eNeuro, October 2017 .
- Matlab software to reproduce Fig.2 and Fig.3 of the paper. Note that you still need to have the CVX package installed in your Matlab distribution (http://cvxr.com/cvx/) to run the files.Read the 'ReadMe' file for more details.
- P.L. Dragotti and Y. Lu, On Sparse Representation in
Fourier and Local Bases, IEEE Trans. on
Information Theory, vol. 60 (12), pp. 7888-7899, December
2014.
- Test Images
- Matlab software: Mac OSX, Windows. If you use this software, we request that you cite:
- A. Scholefield and P.L. Dragotti, Quadtree Structured Image Approximation for Denoising and Interpolation, IEEE Transactions on Image Processing, vol. 23, no. 3, pp. 1226-1239, March 2014.
Check the group GitHub
page for recent software
Older software available below:
An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data
On Sparse Representation in Fourier and Local Bases
Quadtree Structured Image Approximation for Denoising and Interpolation
A Finite Rate of Innovation Algorithm for Fast and Accurate Spike Detection from Two-Photon Calcium Imaging
- ca_transient.zip
- MATLAB implementation for spike train detection
from two-photon calcium imaging applying FRI
techniques. If you use this software, we request that you cite: - J.Onativia, S. Schultz and P.L. Dragotti, A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging, Journal of Neural Engineering, 10 (4), August 2013
- File real_data.m runs the double consistency algorithm on real data and reproduces figure 7 of the journal paper.
- File surrogate_data.m runs the double consistency algorithm on surrogate data.
- File fig3.m reproduces figure 3 of the journal paper.
- File fig9.m reproduces figure 9 of the journal paper.
- A. Gelman, P.L. Dragotti and V. Velisavljevic, Multiview Image Coding using Depth Layers and an Optimized Bit Allocation, IEEE Transactions on Image Processing, vol. 21, no. 9, pp 4092-4105, Sept. 2012.
- A. Gelman, J. Oñativia and P.L. Dragotti, A Fast Layer-based Multiview Image Coding Algorithm, EUSIPCO 2012, August 2012.
Layer-based multiview image compression
MATLAB toolkit to compress multiview images. Contains compiled functions (MEX files) for Windows 32 bits platform. Requires Cygwin.If you use this software, we request that you cite:
- A. Gelman, J. Berent and P.L. Dragotti, Layer-based Sparse Representation of Multiview Images, EURASIP Journal on Advances in Signal Processing, Mar. 2012