/Evaluation-Metrics-for-Image-Fusion

Four evaluation metrics widely used in multi-focus image fusion (MATLAB).

Primary LanguageMATLABMIT LicenseMIT

Evaluation-Metrics-for-Image-Fusion

In MATLAB

Change the code of image reading and run Score.m. The input images should be RGB with the type int.

These four evaluation metrics are widely used in multi-focus image fusion.

The related papers are as follows:

[1]Hossny, M., Nahavandi, S., & Creighton, D. (2008). Comments on'Information measure for performance of image fusion'. Electronics letters, 44(18), 1066-1067. http://dro.deakin.edu.au/eserv/DU:30017835/hossny-commentsoninformation-2008.pdf

[2]Xydeas, C. A., & Petrovic, V. (2000). Objective image fusion performance measure. Electronics letters, 36(4), 308-309. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.7913&rep=rep1&type=pdf

[3]Yang, C., Zhang, J. Q., Wang, X. R., & Liu, X. (2008). A novel similarity based quality metric for image fusion. Information Fusion, 9(2), 156-160. https://www.sciencedirect.com/science/article/abs/pii/S1566253506000704

[4]Chen, Y., & Blum, R. S. (2009). A new automated quality assessment algorithm for image fusion. Image and vision computing, 27(10), 1421-1432. https://www.sciencedirect.com/science/article/pii/S026288560700220X

We also include Lytro dataset here, if it is used in your research, you might have to include the original paper. http://www.irisa.fr/temics/demos/lightField/index.html