at locations where the spread of frequencies is narrow. The proposed 2D multi-scale PC (2D-MSPC) method has been the basis of many IFD studies in various fields such as history, media, basic sciences, and medicine [@Cao2006; @Struc2009; @Zhang2012; @Obara2012; @Mouats2015; @Rijal2015; @Ziaei2014].

The current computation of 2D-MSPC requires several parameters to be tuned [*a priori*]{}. In [@Kovesi1996; @Kovesi1999; @Kovesi2003], several empirical hints have been provided for parameter tuning. But, the selection of parameters either is not explicitly discussed as in [@Cao2006; @Struc2009; @Zhang2012], or is performed manually based on trail and error [@Obara2012; @Mouats2015; @Rijal2015; @Ziaei2014]. In [@Obara2012] and [@Mouats2015], the parameters were manually optimized to increase the visualization of image features. In [@Rijal2015] the parameters were changed by trial and error, and the study found that an improvement in IFD occurred with parameters that maximized the signal-to-noise ratio of the image. In [@Ziaei2014], a set of experiments was also done based on the trial and error, to determine the best, fixed values for computing the maximum moment of PC covariance. The authors reported that there was no clear rule on how the 2D-MSPC parameters should be tuned. Collectively, there is a critical need for a standard parameter tuning method 