diff --git a/imagelib/color_transfer.py b/imagelib/color_transfer.py
index 7d55828..093ed3d 100644
--- a/imagelib/color_transfer.py
+++ b/imagelib/color_transfer.py
@@ -2,9 +2,9 @@ import numpy as np
 import cv2
 
 
-def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, target_mask=None, source_mask=None):
+def reinhard_color_transfer(source, target, clip=False, preserve_paper=False, source_mask=None, target_mask=None):
     """
-    Transfers the color distribution from the source to the target
+    Transfers the color distribution from the target to the source
     image using the mean and standard deviations of the L*a*b*
     color space.
 
@@ -47,18 +47,19 @@ def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, ta
     # convert the images from the RGB to L*ab* color space, being
     # sure to utilizing the floating point data type (note: OpenCV
     # expects floats to be 32-bit, so use that instead of 64-bit)
-    source = cv2.cvtColor(source.astype(np.float32), cv2.COLOR_BGR2LAB)
-    target = cv2.cvtColor(target.astype(np.float32), cv2.COLOR_BGR2LAB)
+    source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB)
+    target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB)
 
     # compute color statistics for the source and target images
     (lMeanSrc, lStdSrc, aMeanSrc, aStdSrc, bMeanSrc, bStdSrc) = lab_image_stats(source, mask=source_mask)
     (lMeanTar, lStdTar, aMeanTar, aStdTar, bMeanTar, bStdTar) = lab_image_stats(target, mask=target_mask)
 
-    # subtract the means from the target image
-    (l, a, b) = cv2.split(target)
-    l -= lMeanTar
-    a -= aMeanTar
-    b -= bMeanTar
+
+    # subtract the means from the source image
+    (l, a, b) = cv2.split(source)
+    l -= lMeanSrc
+    a -= aMeanSrc
+    b -= bMeanSrc
 
     if preserve_paper:
         # scale by the standard deviations using paper proposed factor
@@ -72,9 +73,9 @@ def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, ta
         b = (bStdSrc / bStdTar) * b
 
     # add in the source mean
-    l += lMeanSrc
-    a += aMeanSrc
-    b += bMeanSrc
+    l += lMeanTar
+    a += aMeanTar
+    b += bMeanTar
 
     # clip/scale the pixel intensities to [0, 255] if they fall
     # outside this range
@@ -144,8 +145,8 @@ def lab_image_stats(image, mask=None):
         l, a, b = l[mask == 1], a[mask == 1], b[mask == 1]
 
     l_mean, l_std = np.mean(l), np.std(l)
-    a_mean, a_std = np.mean(l), np.std(l)
-    b_mean, b_std = np.mean(l), np.std(l)
+    a_mean, a_std = np.mean(a), np.std(a)
+    b_mean, b_std = np.mean(b), np.std(b)
 
     # return the color statistics
     return l_mean, l_std, a_mean, a_std, b_mean, b_std
diff --git a/samplelib/SampleProcessor.py b/samplelib/SampleProcessor.py
index cbbbe47..d00b992 100644
--- a/samplelib/SampleProcessor.py
+++ b/samplelib/SampleProcessor.py
@@ -245,8 +245,8 @@ class SampleProcessor(object):
                                                                    ct_sample_bgr,
                                                                    clip=False,
                                                                    preserve_paper=True,
-                                                                   target_mask=img_mask,
-                                                                   source_mask=ct_sample_mask)
+                                                                   source_mask=img_mask,
+                                                                   target_mask=ct_sample_mask)
 
 
                 if normalize_std_dev: