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Deconvolution is an image processing technique that restores the effective object representation [3] [4], allowing to improve images analysis steps such as segmentation [1] or colocalization study [2]. We performed several deconvolution tests on different kinds of datasets. The methodology has been reported in Part 1. Evaluation criteria and results are reported here. References A. Chomik, A. Dieterlen, C. Xu, O. Haeberlé, J.J. Meyer, S. Jacquey, "Quantification in Optical Sectioning Microscopy: A Comparison of Some Deconvolution Algorithms in View of 3D Image Segmentation," Journal of Optics, vol. 28, no. 6, pp. 225-233, December 1997. L. Landmann, "Deconvolution Improves Colocalization Analysis of Multiple Fluorochromes in 3D Confocal Data Sets more than Filtering Techniques," Journal of Microscopy, vol. 208, no. 2, pp. 134-147, November 2002. J.-B. Sibarita, "Deconvolution Microscopy," Advances in Biochemical Engineering/Biotechnology, vol. 95, pp. 201-243, 2005. W. Wallace, L.H. Schaefer, J.R. Swedlow, "A Workingperson's Guide to Deconvolution in Light Microscopy," BioTechniques, vol. 31, no. 5, pp. 1076-1097, November 2001.