diff --git a/assignment2/solution.py b/assignment2/solution.py
index c57dcb3..972bf5c 100644
--- a/assignment2/solution.py
+++ b/assignment2/solution.py
@@ -290,6 +290,7 @@ def two_e():
 def ex3():
     three_a()
     three_b()
+    three_c()
 
 def three_a():
     """
@@ -325,13 +326,13 @@ def three_a():
     axs[0, 0].imshow(lena_grayscale, cmap='gray')
     axs[0, 0].set(title='Orginal image')
     axs[0, 1].imshow(lena_gausssian_noise, cmap='gray')
-    axs[0, 1].set(title='Gaussian noise')
+    axs[0, 1].set(title='Gaussian noise applied')
     axs[1, 1].imshow(denosised_lena, cmap='gray')
-    axs[1, 1].set(title='Denoised lena')
+    axs[1, 1].set(title='Denoised Lena')
     axs[0, 2].imshow(lena_salt_and_pepper, cmap='gray')
-    axs[0, 2].set(title='Salt and Pepper')
+    axs[0, 2].set(title='Salt and Pepper applied')
     axs[1, 2].imshow(desalted_lena, cmap='gray')
-    axs[1, 2].set(title='Desalted lena')
+    axs[1, 2].set(title='Desalted Lena')
     
     plt.show()
 
@@ -357,9 +358,29 @@ def three_b():
     axs[0].set(title='Original')
     axs[1].imshow(museo, cmap='gray')
     axs[1].set(title='Sharpened')
-    plt.savefig('.')
     plt.show()
-    
+
+def three_c():
+    signal = np.zeros(40)
+    signal[15:20] = 1.0
+
+    fig, axs = plt.subplots(1, 4)
+    axs[0].plot(signal)
+    axs[0].set(title='Original')
+
+    signal_sp = uz_image.sp_noise1D(signal, 0.05)
+    axs[1].plot(signal_sp)
+    axs[1].set(title='Corrupted')
+
+    kernel = uz_image.get_gaussian_kernel(1.4)
+    signal_gauss = cv2.filter2D(signal_sp, cv2.CV_64F, kernel)
+    axs[2].plot(signal_gauss)
+    axs[2].set(title='Gauss')
+
+    signal = uz_image.simple_median(signal_sp, 3)
+    axs[3].plot(signal)
+    axs[3].set(title='Median')
+    plt.show()
 
 # ######## #
 # SOLUTION #
diff --git a/assignment2/uz_framework/image.py b/assignment2/uz_framework/image.py
index 6282cd0..58083d0 100644
--- a/assignment2/uz_framework/image.py
+++ b/assignment2/uz_framework/image.py
@@ -294,7 +294,15 @@ def gaussfilter2D(imge: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], s
     kernel = get_gaussian_kernel(sigma)
     kernel = cv2.filter2D(kernel, cv2.CV_64F, kernel)
 
-    
+def simple_median(signal: npt.NDArray[np.float64], width: int):
+    signal = signal.copy()
+    if width % 2 == 0:
+        raise Exception('No u won\'t do that')
+
+    for i in range(len(signal) - int(np.ceil(width/2))):
+        middle_element = int(i + np.floor(width/2))
+        signal[middle_element] = np.median(signal[i:i+width])
+    return signal
 
 def gauss_noise(I, magnitude=.1):
     # input: image, magnitude of noise
@@ -309,10 +317,17 @@ def sp_noise(I, percent=.1):
     # output: modified image
 
     res = I.copy()
-
     res[np.random.rand(I.shape[0], I.shape[1]) < percent / 2] = 1
     res[np.random.rand(I.shape[0], I.shape[1]) < percent / 2] = 0
 
     return res
 
+def sp_noise1D(signal, percent=.1):
+    signal = signal.copy()
+    signal[np.random.rand(signal.shape[0]) < percent / 2] = 2
+    signal[np.random.rand(signal.shape[0]) < percent / 2] = 1
+    signal[np.random.rand(signal.shape[0]) < percent / 2] = 4
+    signal[np.random.rand(signal.shape[0]) < percent / 2] = 0.4
+    return signal
+