diff --git a/converters/ConvertAvatar.py b/converters/ConvertAvatar.py
index 4d7f5d0..0f62657 100644
--- a/converters/ConvertAvatar.py
+++ b/converters/ConvertAvatar.py
@@ -30,7 +30,7 @@ def ConvertFaceAvatar (cfg, prev_temporal_frame_infos, frame_info, next_temporal
         prd_f = cfg.superres_func(cfg.super_resolution_mode, prd_f)
         
     if cfg.sharpen_mode != 0 and cfg.sharpen_amount != 0:
-        prd_f = cfg.sharpen_func ( prd_f, cfg.sharpen_mode, 0.003, cfg.sharpen_amount)
+        prd_f = cfg.sharpen_func ( prd_f, cfg.sharpen_mode, 3, cfg.sharpen_amount)
     
     out_img = np.clip(prd_f, 0.0, 1.0)
 
diff --git a/converters/ConvertMasked.py b/converters/ConvertMasked.py
index 5641d3d..1354875 100644
--- a/converters/ConvertMasked.py
+++ b/converters/ConvertMasked.py
@@ -332,7 +332,7 @@ def ConvertMaskedFace (cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmar
                     out_face_bgr = imagelib.LinearMotionBlur (out_face_bgr, k_size , frame_info.motion_deg)
 
             if cfg.sharpen_mode != 0 and cfg.sharpen_amount != 0:
-                out_face_bgr = cfg.sharpen_func ( out_face_bgr, cfg.sharpen_mode, 0.003, cfg.sharpen_amount)
+                out_face_bgr = cfg.sharpen_func ( out_face_bgr, cfg.sharpen_mode, 3, cfg.sharpen_amount)
             
             new_out = cv2.warpAffine( out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )
             out_img =  np.clip( img_bgr*(1-img_face_mask_aaa) + (new_out*img_face_mask_aaa) , 0, 1.0 )
diff --git a/mainscripts/Converter.py b/mainscripts/Converter.py
index f2b1701..9c0419e 100644
--- a/mainscripts/Converter.py
+++ b/mainscripts/Converter.py
@@ -84,10 +84,10 @@ class ConvertSubprocessor(Subprocessor):
             #therefore forcing active_DeviceConfig to CPU only
             nnlib.active_DeviceConfig = nnlib.DeviceConfig (cpu_only=True)
 
-            def sharpen_func (img, sharpen_mode=0, radius=0.003, amount=150):
-                h,w,c = img.shape
-                radius = max(1, round(w * radius))
-                kernel_size = int((radius * 2) + 1)
+            def sharpen_func (img, sharpen_mode=0, kernel_size=3, amount=150):
+                if kernel_size % 2 == 0:
+                    kernel_size += 1
+                    
                 if sharpen_mode == 1: #box
                     kernel = np.zeros( (kernel_size, kernel_size), dtype=np.float32)
                     kernel[ kernel_size//2, kernel_size//2] = 1.0