diff --git a/src/python/librir/registration/compute_registration.py b/src/python/librir/registration/compute_registration.py
index 8d80fce5..d7f22987 100644
--- a/src/python/librir/registration/compute_registration.py
+++ b/src/python/librir/registration/compute_registration.py
@@ -6,6 +6,7 @@
 ###########################
 
 import os
+from librir.video_io import IRMovie
 import numpy as np
 import pandas as pd
 import cv2
@@ -17,7 +18,7 @@
 ###########################
 
 
-def manage_computation_and_tries(img, regis_obj):
+def manage_computation_and_tries(img, regis_obj: MaskedRegistratorECC):
     """
     Fonction qui calcule les deplacements en x, en y et le niveau de
     confiance. Si l'algorithme ne parvient pas a converger pour une image,
@@ -36,7 +37,7 @@ def manage_computation_and_tries(img, regis_obj):
             if regis_obj.check_median_value(1):
                 regis_obj.define_median_value(1)
         except cv2.error:
-            regis_obj.decrease_median_value(0.01)
+            regis_obj.decrease_median(0.01)
             nb_try += 1
         if nb_try > 0:
             print("try number : {}".format(nb_try))
@@ -49,7 +50,7 @@ def manage_computation_and_tries(img, regis_obj):
 
 
 def compute_confidence_and_x_y_trajectories(
-    pulse_obj, view, lower_bound, upper_bound, calibration, *args
+    ir_movie: IRMovie, view, lower_bound, upper_bound, calibration, *args
 ):
     """
     Fonction qui permet d'estimer les deplacements (x,y) des pixels
@@ -61,10 +62,9 @@ def compute_confidence_and_x_y_trajectories(
         is_div = False
 
     for img_number in range(lower_bound, upper_bound + 1):
-
         if img_number % 50 == 0:
             print(img_number)
-        img = pulse_obj.load_pos(img_number, calibration)
+        img = ir_movie.load_pos(img_number, calibration)
 
         if is_div:
             regis_obj_up = manage_computation_and_tries(img, args[0])
@@ -310,7 +310,6 @@ def compute_registration_ir(view_name, pulse_or_filename, outfile):
 
 
 if __name__ == "__main__":
-
     # Arguments: view_name pulse_or_file out_file
     import sys
 
diff --git a/src/python/librir/registration/masked_registration_ecc.py b/src/python/librir/registration/masked_registration_ecc.py
index 59988d74..75029a9a 100644
--- a/src/python/librir/registration/masked_registration_ecc.py
+++ b/src/python/librir/registration/masked_registration_ecc.py
@@ -5,9 +5,12 @@
 @author: CS266247
 """
 
+from librir.signal_processing.rir_signal_processing import (
+    find_median_pixel,
+    gaussian_filter,
+    translate,
+)
 import numpy as np
-import librir as lr
-import librir.signal_processing
 import cv2
 
 ########################
@@ -62,7 +65,7 @@ def __init__(
         if ref is not None and pre_process is not None:
             self.ref = pre_process(ref)
         if sigma > 0 and self.ref is not None:
-            self.ref = lr.signal_processing.gaussian_filter(self.ref, sigma)
+            self.ref = gaussian_filter(self.ref, sigma)
         self.mask_ref_img = None
         self.window_factorH = window_factorh
         self.window_factorV = window_factorv
@@ -82,7 +85,7 @@ def start(self, img):
         if self.pre_process is not None:
             img = self.pre_process(img)
         if self.sigma > 0:
-            img = lr.signal_processing.gaussian_filter(img, self.sigma)
+            img = gaussian_filter(img, self.sigma)
 
         self.ref_img = img[
             self.startY : self.startY + self.subH, self.startX : self.startX + self.subW
@@ -102,7 +105,7 @@ def compute(self, img):
         if self.pre_process is not None:
             img = self.pre_process(img)
         if self.sigma > 0:
-            img = lr.signal_processing.gaussian_filter(img, self.sigma)
+            img = gaussian_filter(img, self.sigma)
         new_im = img[
             self.startY : self.startY + self.subH, self.startX : self.startX + self.subW
         ].copy()
@@ -140,11 +143,8 @@ def compute(self, img):
             mask = self.mask
 
         if self.median < 1:
-
-            thresh1 = lr.signal_processing.find_median_pixel(new_im, self.median, mask)
-            thresh2 = lr.signal_processing.find_median_pixel(
-                self.ref_img, self.median, mask
-            )
+            thresh1 = find_median_pixel(new_im, self.median, mask)
+            thresh2 = find_median_pixel(self.ref_img, self.median, mask)
             thresh = max(thresh1, thresh2)
 
             m = (im1 > thresh) | (im2 > thresh)
@@ -183,7 +183,7 @@ def compute(self, img):
                 print("confidence:", self.conf_thresh)
             if confidence < self.conf_thresh:
                 print("reset at confidence ", len(self.x), confidence)
-                new_im = lr.signal_processing.translate(new_im, -shift[1], -shift[0])
+                new_im = translate(new_im, -shift[1], -shift[0])
                 self.ref_img = new_im
                 self.start_mat = np.eye(2, 3, dtype=np.float32)