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Installation_Use_Test_README.txt
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% M Estimator (homogeneity, ENL, Mean)
% Reproducible research for article:
% L. Gomez, R. Ospina, A. C. Frery
% "Unassisted quantitative evaluation of despeckling filters"
% Work by L. Gomez, R. Ospina, A. C. Frery; codified to Matlab by L. Gomez
% Run well on Matlab 2014a (only tested on Windows 7 Enterprise 64 bits)
% February 2017
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Please use it freely and cite the reference paper mentioned above.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Some technical issues:
The program has been tested only on a Windows-7 Enterprise 64Bits.
There is a complete front-ed written in Matlab. So, Matlab is required to execute the codes.
Matlab version recommended: 2014a (64 bits).THe program may run on older Matlab versions but FANS filter requires an updated version.
FANS filter files are icluded in the zip file (this is a software freely available from Authors of FANS filter;please, also cite them if you use this program).
Take care that FANS filter requires some pre-compiled executable files. The ones included in our implementation work exclusively on Windows 64 bits.
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Program instalation:
To use the program, unzip the file Detect_Structure_Matlab.7z in a folder.
A folder is created: \data. This folder contains the data to use with the program.
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Running the program:
In the folder, just click on the file "ENL_Mean_Estimator.m", or open Matlab and load the "ENL_Mean_Estimator.m file" and execute it as any Matlab file.
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Using the program:
The natural flow is the following:
- Select Data: load data,
- leave the default input parameters (except the ENL one: it must be fitted to the ENL of the data to test; it is supposed the user know the ENL of the data).
- select the mode of data: Intensity or Amplitude.
- Push "Calculate" and see the results for H1 instance on the corresponding fields.
- Generate a H0 instance: Push "Build H0".
- Push "Calculate" and see the results for H0 instance on the corresponding fields.
Get the standard estimators: Mean, Std, and ENL:
- From a previous data loaded, just select the image (Noisy, Filtered or Ratio) and a ROI can be selected. The measured values (Mean, Std., ENL) will appear in the related fields.
- The same may be done also for H0 random generated data.
Running the optimizer:
- leave the default input parameters.
- Push "Calculate" and then, push "FANS Optimum" to see the optimal filtered result.
Notice that images can be enlarged (push "Enlarge") for a better visual inspection.
Feel free to "play" with the program and, please, report any problem to luis.gomez@ulpgc.es
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% M Estimator (homogeneity, ENL, Mean)
% Reproducible research for article:
% L. Gomez, R. Ospina, A. C. Frery
% "Unassisted quantitative evaluation of despeckling filters"
% Work by L. Gomez, R. Ospina, A. C. Frery; codified to Matlab by L. Gomez
% Run well on Matlab 2014a (only tested on Windows 7 Enterprise 64 bits)
% February 2017
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Please use it freely and cite the reference paper mentioned above.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%