Noise in images appears as unwanted graininess or distortion, often caused by low lighting, compression artifacts, or sensor limitations. Smoothing techniques help reduce noise while preserving important image details.

Gaussian Blurring (Smoothing Noise)

cv2.GaussianBlur function applies a Gaussian blur, which smooths the image by averaging pixel values using a Gaussian kernel (a weighted average that gives more importance to central pixels):

• cv2.GaussianBlur(src, ksize, sigmaX):

  • src: the source image to be blurred;

  • ksize: kernel size in the format (width, height), both values must be odd (e.g., (5, 5));

  • sigmaX: standard deviation in the X direction; controls the amount of blur.

• The function reduces image noise and detail by convolving the image with a Gaussian function, which is useful in tasks like edge detection or pre-processing before thresholding.

Median Blurring (Salt-and-Pepper Noise Removal)

cv2.medianBlur function applies a median filter, which replaces each pixel value with the median value of the neighboring pixels in the kernel window:

• cv2.medianBlur(src, ksize):

  • src: the source image to be filtered;

  • ksize: size of the square kernel (must be an odd integer, e.g., 3, 5, 7).

• The median blur is especially effective at removing salt-and-pepper noise, as it preserves edges while eliminating isolated noisy pixels.