Image filtering functions are often used to pre-process or adjust an image before performing more complex operations. These operations help reduce noise or unwanted variances of an image or threshold.
There are three filters available in the OpenCV-Python library.
- Gaussian Blur Filter
- Erosion Blur Filter
- Dilation Blur Filter
Image Smoothing techniques help us in reducing the noise in an image. In OpenCV, image smoothing (also called blurring) could be done in many ways. We will see the GaussianBlur() method in detail in this post.
Gaussian Blur
The Gaussian Blur filter smooths the image by averaging pixel values with its neighbors. It’s called the Gaussian Blur because an average has the Gaussian falloff effect.
What that means is that pixels that are closer to a target pixel have a higher influence on the average than pixels that are far away. This is how the smoothing works. It is often used as a decent way to smooth out noise in an image as a precursor to other processing.
#python #python cv2 #gaussianblur #opencv #opencv-python
