diff --git a/image_fusion/Image_Registration_test.py b/image_fusion/Image_Registration_test.py
index e5e5b71..7161906 100644
--- a/image_fusion/Image_Registration_test.py
+++ b/image_fusion/Image_Registration_test.py
@@ -49,6 +49,10 @@ def Images_matching(img_base, img_target):
     """
     start = time.time()
     orb = cv2.ORB_create()
+    
+    # 对可见光图像进行对比度拉伸
+    # img_base = truncated_linear_stretch(img_base)
+    
     img_base = cv2.cvtColor(img_base, cv2.COLOR_BGR2GRAY)
     sift = cv2.SIFT_create()
     # 使用sift算子计算特征点和特征点周围的特征向量
@@ -56,7 +60,9 @@ def Images_matching(img_base, img_target):
     kp1, des1 = sift.detectAndCompute(img_base, None)  # 1136    1136, 64
     kp2, des2 = sift.detectAndCompute(img_target, None)
     en1 = time.time()
+    
     # print(en1 - st1, "特征提取")
+    
     # 进行KNN特征匹配
     # FLANN_INDEX_KDTREE = 0  # 建立FLANN匹配器的参数
     # indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)  # 配置索引，密度树的数量为5
@@ -68,6 +74,7 @@ def Images_matching(img_base, img_target):
     # search_params = dict(checks=50)
     # flann = cv2.FlannBasedMatcher(index_params, search_params)
     # matches = flann.knnMatch(des1, des2, k=2)
+    
     st2 = time.time()
     matcher = cv2.BFMatcher()
     matches = matcher.knnMatch(des1, des2, k=2)
@@ -143,7 +150,7 @@ def main(matchimg_vi, matchimg_in):
             return 0, None, 0
         else:
             matched_ni = cv2.warpPerspective(orimg_in, H, (w, h))
-            # matched_ni,left,right,top,bottom=removeBlackBorder(matched_ni)
+            matched_ni,left,right,top,bottom=removeBlackBorder(matched_ni)
             # fusion = fusions(orimg_vi[left:right, top:bottom], matched_ni)
             fusion = fusions(orimg_vi, matched_ni)