Python OpenCV Blur and Sharpen Images Instantly

Blurring and sharpening are fundamental image processing techniques used in computer vision. With OpenCV, we can easily apply these effects to images to enhance or smooth details instantly.

Step 1: Install Required Libraries

Ensure OpenCV is installed:

pip install opencv-python numpy

Step 2: Import Libraries and Load Image

import cv2

import numpy as np

 # Load an image

image = cv2.imread(“image.jpg”)

Step 3: Apply Blurring Techniques

Blurring helps reduce noise and smooth images. OpenCV provides multiple methods for blurring:

1. Gaussian Blur

blurred_gaussian = cv2.GaussianBlur(image, (15, 15), 0)

2. Median Blur

blurred_median = cv2.medianBlur(image, 5)

3. Bilateral Filter (Preserves Edges)

blurred_bilateral = cv2.bilateralFilter(image, 9, 75, 75)

Step 4: Apply Sharpening Techniques

Sharpening enhances edges and details in an image. We can achieve this using kernel filtering.

1. Define a Sharpening Kernel

sharpen_kernel = np.array([[0, -1, 0],

                            [-1, 5,-1],

                            [0, -1, 0]])

sharpened_image = cv2.filter2D(image, -1, sharpen_kernel)

Step 5: Display Results

cv2.imshow(“Original Image”, image)

cv2.imshow(“Gaussian Blur”, blurred_gaussian)

cv2.imshow(“Median Blur”, blurred_median)

cv2.imshow(“Bilateral Blur”, blurred_bilateral)

cv2.imshow(“Sharpened Image”, sharpened_image)

 cv2.waitKey(0)

cv2.destroyAllWindows()

Conclusion

Blurring and sharpening are essential techniques in image processing. OpenCV provides powerful functions to easily apply these effects, enhancing images for better visual representation and analysis.