# mrcImageNormalizing

## Usage

    Usage: mrcImageNormalizing
    Options:
        [-i[nput]            In                  (NULL      ).as(inFile              ) ] :Essential :InputDataFile
        [-o[utput]           Out                 (NULL      ).as(outFile             ) ] :Essential :OutputDataFile
        [-A                  A                   (1.0       ).as(Real                ) ] :Optional  :A
        [-B                  B                   (0.0       ).as(Real                ) ] :Optional  :B
        [-ContourMin         ContourMin          (0.0       ).as(Real                ) ] :Optional  :ContourMin
        [-ContourMax         ContourMax          (1.0       ).as(Real                ) ] :Optional  :ContourMax
        [-ContourSolvent     ContourSolvent      (0.0       ).as(Real                ) ] :Optional  :ContourSolvent
        [-c[onfig]           configFile          (NULL      ).as(inFile              ) ] :Optional  :ConfigurationFile
        [-m[ode]             mode                (0         ).as(Integer             ) ] :Optional  :Mode
    ----- mode -----
    ----- Mode for lmrcImageNormalizing -----
      0: Double Exponential: Solvent and Object
             Fitting histgram to double exponentials as Solvent and Object
    		   data = A*(data-MeanOfSolvent)/(MeanOfObject-MeanOfSolvent) + B
      1: Min-Max: Background and Object
    		   data = A*(data-Min)/(Max-Min) + B
      2: Contour
    		   data = A*(data-ContourMin)/(ContourMax-ContourMin) + B
      3: Contour and Solvent
    		   if data < ContourSolvent, data = ContourSolvent.  After this, calculate the below.
    		   data = A*(data-ContourMin)/(ContourMax-ContourMin) + B
      4: No Estimation
    		   data = A*data + B
      5: Assume the density as gaussion.
    		   data = A*Normalized(data) + B , where normalized means (average=0, SD=1)
      6: Min-75percent: Background and Object(75)
    		   data = A*(data-Min)/(75percent-Min)x0.75 + B