diff --git a/core/imagelib/__init__.py b/core/imagelib/__init__.py
index 23fb889..a4f1482 100644
--- a/core/imagelib/__init__.py
+++ b/core/imagelib/__init__.py
@@ -22,6 +22,7 @@ from .blursharpen import LinearMotionBlur, blursharpen
 
 from .filters import apply_random_rgb_levels, \
                      apply_random_hsv_shift, \
+                     apply_random_sharpen, \
                      apply_random_motion_blur, \
                      apply_random_gaussian_blur, \
                      apply_random_nearest_resize, \
diff --git a/core/imagelib/filters.py b/core/imagelib/filters.py
index 19655bf..eb45ecb 100644
--- a/core/imagelib/filters.py
+++ b/core/imagelib/filters.py
@@ -1,47 +1,65 @@
 import numpy as np
-from .blursharpen import LinearMotionBlur
+from .blursharpen import LinearMotionBlur, blursharpen
 import cv2
 
 def apply_random_rgb_levels(img, mask=None, rnd_state=None):
     if rnd_state is None:
         rnd_state = np.random
     np_rnd = rnd_state.rand
-    
+
     inBlack  = np.array([np_rnd()*0.25    , np_rnd()*0.25    , np_rnd()*0.25], dtype=np.float32)
     inWhite  = np.array([1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25], dtype=np.float32)
     inGamma  = np.array([0.5+np_rnd(), 0.5+np_rnd(), 0.5+np_rnd()], dtype=np.float32)
-    
+
     outBlack  = np.array([np_rnd()*0.25    , np_rnd()*0.25    , np_rnd()*0.25], dtype=np.float32)
     outWhite  = np.array([1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25], dtype=np.float32)
 
     result = np.clip( (img - inBlack) / (inWhite - inBlack), 0, 1 )
     result = ( result ** (1/inGamma) ) *  (outWhite - outBlack) + outBlack
     result = np.clip(result, 0, 1)
-    
+
     if mask is not None:
         result = img*(1-mask) + result*mask
-        
+
     return result
-    
+
 def apply_random_hsv_shift(img, mask=None, rnd_state=None):
     if rnd_state is None:
         rnd_state = np.random
-    
+
     h, s, v = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
     h = ( h + rnd_state.randint(360) ) % 360
     s = np.clip ( s + rnd_state.random()-0.5, 0, 1 )
-    v = np.clip ( v + rnd_state.random()-0.5, 0, 1 )                    
-    
+    v = np.clip ( v + rnd_state.random()-0.5, 0, 1 )
+
     result = np.clip( cv2.cvtColor(cv2.merge([h, s, v]), cv2.COLOR_HSV2BGR) , 0, 1 )
     if mask is not None:
         result = img*(1-mask) + result*mask
-        
+
     return result
-    
+
+def apply_random_sharpen( img, chance, kernel_max_size, mask=None, rnd_state=None ):
+    if rnd_state is None:
+        rnd_state = np.random
+
+    sharp_rnd_kernel = rnd_state.randint(kernel_max_size)+1
+
+    result = img
+    if rnd_state.randint(100) < np.clip(chance, 0, 100):
+        if rnd_state.randint(2) == 0:
+            result = blursharpen(result, 1, sharp_rnd_kernel, rnd_state.randint(10) )
+        else:
+            result = blursharpen(result, 2, sharp_rnd_kernel, rnd_state.randint(50) )
+                
+        if mask is not None:
+            result = img*(1-mask) + result*mask
+
+    return result
+
 def apply_random_motion_blur( img, chance, mb_max_size, mask=None, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
-    
+
     mblur_rnd_kernel = rnd_state.randint(mb_max_size)+1
     mblur_rnd_deg    = rnd_state.randint(360)
 
@@ -50,22 +68,22 @@ def apply_random_motion_blur( img, chance, mb_max_size, mask=None, rnd_state=Non
         result = LinearMotionBlur (result, mblur_rnd_kernel, mblur_rnd_deg )
         if mask is not None:
             result = img*(1-mask) + result*mask
-        
+
     return result
-    
+
 def apply_random_gaussian_blur( img, chance, kernel_max_size, mask=None, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
-        
+
     result = img
     if rnd_state.randint(100) < np.clip(chance, 0, 100):
         gblur_rnd_kernel = rnd_state.randint(kernel_max_size)*2+1
         result = cv2.GaussianBlur(result, (gblur_rnd_kernel,)*2 , 0)
         if mask is not None:
             result = img*(1-mask) + result*mask
-            
+
     return result
-    
+
 def apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_LINEAR, mask=None, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
@@ -73,24 +91,24 @@ def apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_LINE
     result = img
     if rnd_state.randint(100) < np.clip(chance, 0, 100):
         h,w,c = result.shape
-        
+
         trg = rnd_state.rand()
-        rw = w - int( trg * int(w*(max_size_per/100.0)) )                        
-        rh = h - int( trg * int(h*(max_size_per/100.0)) )   
-             
+        rw = w - int( trg * int(w*(max_size_per/100.0)) )
+        rh = h - int( trg * int(h*(max_size_per/100.0)) )
+
         result = cv2.resize (result, (rw,rh), interpolation=interpolation )
         result = cv2.resize (result, (w,h), interpolation=interpolation )
         if mask is not None:
             result = img*(1-mask) + result*mask
-            
+
     return result
-    
+
 def apply_random_nearest_resize( img, chance, max_size_per, mask=None, rnd_state=None ):
     return apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_NEAREST, mask=mask, rnd_state=rnd_state )
-    
+
 def apply_random_bilinear_resize( img, chance, max_size_per, mask=None, rnd_state=None ):
     return apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_LINEAR, mask=mask, rnd_state=rnd_state )
-    
+
 def apply_random_jpeg_compress( img, chance, mask=None, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
@@ -98,14 +116,14 @@ def apply_random_jpeg_compress( img, chance, mask=None, rnd_state=None ):
     result = img
     if rnd_state.randint(100) < np.clip(chance, 0, 100):
         h,w,c = result.shape
-        
+
         quality = rnd_state.randint(10,101)
-        
+
         ret, result = cv2.imencode('.jpg', np.clip(img*255, 0,255).astype(np.uint8), [int(cv2.IMWRITE_JPEG_QUALITY), quality] )
         if ret == True:
             result = cv2.imdecode(result, flags=cv2.IMREAD_UNCHANGED)
             result = result.astype(np.float32) / 255.0
             if mask is not None:
                 result = img*(1-mask) + result*mask
-                
+
     return result
\ No newline at end of file