Blurring and smoothing (Gaussian, median, bilateral) in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of blurring or smoothing an image?

Blurring or smoothing reduces noise and small details in an image, making it easier to analyze or process by removing unwanted variations.

beginner

How does Gaussian blur work on an image?

Gaussian blur uses a weighted average where pixels near the center have more influence, following a bell-shaped curve, to smooth the image gently.

intermediate

What makes median blur different from Gaussian blur?

Median blur replaces each pixel with the median value of its neighbors, which is very effective at removing salt-and-pepper noise without blurring edges much.

intermediate

Explain the bilateral filter and its advantage.

Bilateral filter smooths images while keeping edges sharp by considering both spatial closeness and pixel intensity difference, preserving important details.

beginner

Which blurring method is best for removing salt-and-pepper noise?

Median blur is best for salt-and-pepper noise because it replaces pixels with the median of neighbors, effectively removing outliers without blurring edges.

Which blurring technique uses a bell-shaped curve to weight neighboring pixels?

A Box blur

B Median blur

C Bilateral filter

D Gaussian blur

Gaussian blur weights pixels based on a Gaussian (bell-shaped) distribution, giving more importance to closer pixels.

Which filter is best at preserving edges while smoothing?

A Gaussian blur

B Bilateral filter

C Median blur

D Average blur

Bilateral filter preserves edges by considering both spatial closeness and intensity differences.

What does median blur replace each pixel with?

A Median of neighbors

B Mean of neighbors

C Maximum of neighbors

D Minimum of neighbors

Median blur replaces each pixel with the median value of its neighboring pixels.

Which noise type is median blur especially good at removing?

A Gaussian noise

B Speckle noise

C Salt-and-pepper noise

D Poisson noise

Median blur effectively removes salt-and-pepper noise by replacing outlier pixels.

What is a downside of Gaussian blur compared to median blur?

A It blurs edges more

B It cannot remove noise

C It is slower to compute

D It only works on color images

Gaussian blur smooths edges more, which can reduce sharpness compared to median blur.

Describe the differences between Gaussian, median, and bilateral blurring methods and when you might use each.

Think about how each method treats edges and noise.

Explain why bilateral filtering is useful in image processing compared to simple Gaussian blur.

Focus on edge preservation and noise smoothing.