Noise Reduction and Smoothing
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.
blurred = cv2.GaussianBlur(image, ksize, sigmaX)
In cv2.GaussianBlur(), parameter sigmaX, is the Gaussian kernel standard deviation in the X direction, and the same parameter in the Y direction (sigmaY) has the value 0 as default. When both sigmaX and sigmaY have a value of 0, the standard deviation is calculated from kernel size.
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.
median_blurred = cv2.medianBlur(image, ksize)
Swipe to start coding
You are given the image variable of the noisy image of the puppy:
- Apply Gaussian Blur and store result in
gaussian_blurredvariable; - Apply Gaussian Blur and store result in
median_blurredvariable.
Solution
Thanks for your feedback!
single
Ask AI
Ask AI
Ask anything or try one of the suggested questions to begin our chat
What is the difference between Gaussian blur and median blur?
When should I use Gaussian blur versus median blur?
Can you explain how to choose the right kernel size for these filters?
Awesome!
Completion rate improved to 3.45Noise Reduction and Smoothing
Swipe to show menu
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.
blurred = cv2.GaussianBlur(image, ksize, sigmaX)
In cv2.GaussianBlur(), parameter sigmaX, is the Gaussian kernel standard deviation in the X direction, and the same parameter in the Y direction (sigmaY) has the value 0 as default. When both sigmaX and sigmaY have a value of 0, the standard deviation is calculated from kernel size.
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.
median_blurred = cv2.medianBlur(image, ksize)
Swipe to start coding
You are given the image variable of the noisy image of the puppy:
- Apply Gaussian Blur and store result in
gaussian_blurredvariable; - Apply Gaussian Blur and store result in
median_blurredvariable.
Solution
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
single
