diff --git a/Python/opencv-image-processing/Hough_probablistic_transform.py b/Python/opencv-image-processing/Hough_probablistic_transform.py
new file mode 100644
index 00000000..ea2be1ef
--- /dev/null
+++ b/Python/opencv-image-processing/Hough_probablistic_transform.py
@@ -0,0 +1,17 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('images/roads.png')
+gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+edges = cv2.Canny(img,50,150,apertureSize = 3)
+cv2.imshow('Edges',edges)
+lines = cv2.HoughLinesP(edges, 1 ,np.pi/180,100,minLineLength = 100,
+                        maxLineGap = 10)
+
+for line in lines:
+    x1,y1,x2,y2 = line[0]
+    cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)
+    
+cv2.imshow('image',img)
+k = cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/Python/opencv-image-processing/images/roads.png b/Python/opencv-image-processing/images/roads.png
new file mode 100644
index 00000000..8f033dd4
Binary files /dev/null and b/Python/opencv-image-processing/images/roads.png differ
diff --git a/Python/opencv-image-processing/smoothing_images_and_image_blurring.py b/Python/opencv-image-processing/smoothing_images_and_image_blurring.py
new file mode 100644
index 00000000..81a4c7b3
--- /dev/null
+++ b/Python/opencv-image-processing/smoothing_images_and_image_blurring.py
@@ -0,0 +1,34 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+img = cv2.imread('lena.jpg')
+img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+
+
+kernel = np.ones((5,5),np.float32)/25
+
+dst = cv2.filter2D(img,-1,kernel)
+
+blur = cv2.blur(img,(5,5))
+
+guassian_blur = cv2.GaussianBlur(img,(5,5),0)
+
+median_blur = cv2.medianBlur(img,5)#to remove salt_and_pepper_noise
+
+bilateralFilter = cv2.bilateralFilter(img,9, 75,75)
+
+titles = ['images','2D Convolution','blur','Gaussian Blur','median','bilateralFilter']
+images= [ img,dst,blur,guassian_blur, median_blur,bilateralFilter]
+
+
+
+
+
+for i in range(len(images)):
+    plt.subplot(2,3,i+1)
+    plt.imshow(images[i],cmap='gray')
+    plt.title(titles[i])
+    plt.xticks([]),plt.yticks([])
+
+plt.show()