diff --git a/daq/src/aaredaq/daq.py b/daq/src/aaredaq/daq.py
index 217c9ba4..cb2496d9 100644
--- a/daq/src/aaredaq/daq.py
+++ b/daq/src/aaredaq/daq.py
@@ -763,54 +763,14 @@ class AareDAQ:
         # Subtract the "without feature" image from the "with feature" image
         diff_image = cv2.absdiff(gray_with_feature, gray_without_feature)
 
-        # Threshold the difference to isolate the feature
-
-        def flatfield_correction(raw_image, flat_image, dark_image=None):
-            """
-            Apply flat-field correction to remove uneven illumination
-
-            Args:
-                raw_image: Your actual image with crystal
-                flat_image: Image of uniform illumination (no sample)
-                dark_image: Dark frame (camera with no light), optional
-            """
-            # Convert to float for calculations
-            raw = raw_image.astype(np.float32)
-            flat = flat_image.astype(np.float32)
-
-            if dark_image is not None:
-                dark = dark_image.astype(np.float32)
-                # Subtract dark frame from both
-                raw_corrected = raw - dark
-                flat_corrected = flat - dark
-            else:
-                raw_corrected = raw
-                flat_corrected = flat
-
-            # Avoid division by zero
-            flat_corrected[flat_corrected == 0] = 1
-
-            # Apply correction
-            mean_flat = np.mean(flat_corrected)
-            corrected = (raw_corrected / flat_corrected) * mean_flat
-
-            # Convert back to original dtype
-            return np.clip(corrected, 0, 255).astype(raw_image.dtype)
-
-        #_, thresh = cv2.threshold(diff_image, 30, 255, cv2.THRESH_BINARY)
         thresh_value, thresh = cv2.threshold(diff_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
 
-        #flatfield_correction_image = flatfield_correction(gray_with_feature, gray_without_feature)
-        #adapt_thresh = cv2.adaptiveThreshold(gray_with_feature, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,
-        #                                      11, 2)
-        #thresh_value, thresh = cv2.threshold(flatfield_correction_image, 0, 255,
-        #                                            cv2.THRESH_BINARY + cv2.THRESH_OTSU)
         print(f'Thresh value: {thresh_value}')
         if filename is not None:
             #cv2.imwrite(f"{filename}_curr_image_colour.jpg", curr_image)
-            #cv2.imwrite(f"{filename}_curr_image.tiff", gray_with_feature)
-            cv2.imwrite(f"{filename}_diff.tiff", diff_image)
-            cv2.imwrite(f"{filename}_thresh.tiff", thresh)
+            cv2.imwrite(f"{filename}_curr_image.jpg", gray_with_feature)
+            cv2.imwrite(f"{filename}_diff.jpg", diff_image)
+            cv2.imwrite(f"{filename}_thresh.jpg", thresh)
             #cv2.imwrite(f"{filename}_adaptive.tiff", adapt_thresh)
 
         # # Find contours of the detected feature
@@ -834,10 +794,15 @@ class AareDAQ:
             raise LoopCenteringFailed()
 
         geom = self.sample_geometry
-
-        coord = geom.picture_to_smargon(
-            Coordinate(x=leftmost_point[0], y=leftmost_point[1])
-        )
+        if iteration == 0:
+            coord = geom.picture_to_smargon(
+                Coordinate(x=leftmost_point[0], y=leftmost_point[1])
+            )
+        else:
+            #TODO only move x after first iteration
+            coord = geom.picture_to_smargon(
+                Coordinate(x=leftmost_point[0], y=leftmost_point[1])
+            )
         return SmargonCoordinate(sh_mm=coord)
 
     def __loop_center_sequence(self, sample_id: int | None = None) -> bool: