diff --git a/Code/Dyschromasie.py b/Code/Dyschromasie.py
index fcf881b..a328cb5 100644
--- a/Code/Dyschromasie.py
+++ b/Code/Dyschromasie.py
@@ -32,38 +32,26 @@ def reverseGammaCorrection(v_reverse):
         print("Ungültiger Wert!!!")
         return 1
 
-
 '''
-           0.4124564  0.3575761  0.1804375    Transformationsmatrix fuer XYZ Werte aus gegebenen RGB Werten!
-RGB2XYZ =  0.2126729  0.7151522  0.0721750   
-           0.0193339  0.1191920  0.9503041
+        0.31399022 0.63951294 0.04649755   Transformationsmatrix zum Konvertieren vom linearen RGB zum LMS Farbraum
+T   =   0.15537241 0.75789446 0.08670142   Multiplikation aus Brucelindbloom und Hunt-Pointer-Estevez Matrixen 
+        0.01775239 0.10944209 0.87256922   T*RGB_Farbverktor = LMS_Farbvektor
 '''
 
-RGB2XYZ = np.array(
-    [[0.4124564, 0.3575761, 0.1804375],
-     [0.2126729, 0.7151522, 0.0721750],
-     [0.0193339, 0.1191920, 0.9503041]])
+T = np.array([[0.31399022,0.63951294,0.04649755],
+              [0.15537241,0.75789446,0.08670142],
+              [0.01775239,0.10944209,0.87256922]])
 
 '''
-            3.2404542 -1.5371385 -0.4985314   Transformationsmatrix fuer RGB Werte aus gegebenen XYZ Werten!
-XYZ2RGB =  -0.9692660  1.8760108  0.0415560   (RGB nur ganzzahlig --> Runden!!)
-            0.0556434 -0.2040259  1.0572252  
+               5.47221206 −4.6419601   0.16963708    Rücktransformationsmatrix (Inverse von T)
+T_reversed  = -1.1252419  2.29317094  −0.1678952     T_reversed Ü LMS_Farbvektor = RBG_Farbvektor
+               0.02980165 −0.19318073  1.16364789
 '''
 
-XYZ2RGB = np.array(
-    [[3.2404542, -1.5371385, -0.4985314],
-     [-0.9692660, 1.8760108, 0.0415560],
-     [0.0556434, -0.2040259, 1.0572252]])
+T_reversed = np.array([[5.47221206,-4.6419601,0.16963708],
+                       [-1.1252419,2.29317094,-0.1678952],
+                       [0.02980165,-0.19318073,1.16364789]])
 
-'''
-            0.4002     0.7076    −0.0808     Transformationsmatrix fuer LMS Werte aus gegebenen XYZ Werten
-M_HPE =    −0.2263     1.1653     0.0457  
-            0          0          0.9182  
-'''
-
-M_HPE = np.array([[0.4002, 0.7076, -0.0808],
-                  [-0.2263, 1.1653, 0.0457],
-                  [0, 0, 0.9182]])
 
 # for i in range(rows):           #Durchgehen aller Pixel des Bildes
 #   for j in range(cols):