Blurring and smoothing (Gaussian, median, bilateral) in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Gaussian Blur on a simple image array

Given the following 3x3 grayscale image array and applying a Gaussian blur with kernel size 3 and sigma 1, what is the center pixel value after blurring?

Computer Vision

import cv2
import numpy as np

image = np.array([[10, 20, 30],
                  [20, 30, 40],
                  [30, 40, 50]], dtype=np.uint8)

blurred = cv2.GaussianBlur(image, (3, 3), 1)
center_pixel = blurred[1, 1]
print(center_pixel)

A 30

B 28

C 29

D 31

💡 Hint

Gaussian blur smooths pixels weighted by a Gaussian kernel; center pixel is a weighted average.

The Gaussian blur with kernel size 3 and sigma 1 computes a weighted average around the center pixel. The exact value is 29.

🧠 Conceptual

intermediate

Effect of median blur on salt-and-pepper noise

Which statement best describes the effect of applying a median blur filter on an image corrupted with salt-and-pepper noise?

A Median blur replaces each pixel with the average of its neighbors, smoothing the noise.

B Median blur replaces each pixel with the median of its neighbors, effectively removing salt-and-pepper noise.

C Median blur sharpens edges while reducing noise.

D Median blur applies a Gaussian weighted average, which is ineffective against salt-and-pepper noise.

💡 Hint

Think about how median values handle extreme pixel values.

Median blur replaces each pixel with the median of its neighbors, which removes isolated extreme values like salt-and-pepper noise effectively.

❓ Model Choice

advanced

Choosing the best smoothing filter for edge preservation

You want to smooth an image to reduce noise but keep edges sharp and clear. Which filter is the best choice?

A Bilateral filter

B Median blur

C Gaussian blur

D Average blur

💡 Hint

Consider which filter uses both spatial and intensity information.

Bilateral filter smooths while preserving edges by considering both pixel distance and intensity differences.

❓ Hyperparameter

advanced

Effect of increasing sigma in Gaussian blur

What happens to the image when you increase the sigma value in a Gaussian blur filter while keeping the kernel size constant?

A The filter ignores pixels farther from the center.

B The image noise increases due to less smoothing.

C The image becomes sharper with more details preserved.

D The image becomes more blurred with stronger smoothing effect.

💡 Hint

Sigma controls the spread of the Gaussian kernel.

Increasing sigma makes the Gaussian kernel wider, causing more smoothing and blur.

🔧 Debug

expert

Identifying error in bilateral filter usage

What error will this code produce when applying bilateral filter with the given parameters? import cv2 import numpy as np image = np.ones((5,5), dtype=np.uint8)*100 blurred = cv2.bilateralFilter(image, d=0, sigmaColor=75, sigmaSpace=75) print(blurred)

Computer Vision

import cv2
import numpy as np

image = np.ones((5,5), dtype=np.uint8)*100
blurred = cv2.bilateralFilter(image, d=0, sigmaColor=75, sigmaSpace=75)
print(blurred)

A No error, prints the blurred image array

B cv2.error: d must be positive or -1

C TypeError: unsupported operand type(s) for *: 'int' and 'str'

D ValueError: sigmaColor must be less than 50

💡 Hint

Check the meaning of parameter d in bilateralFilter.

No error occurs because when d is non-positive (including 0), it is automatically computed from sigmaSpace.

Practice

1. Which filter is best for removing salt-and-pepper noise while preserving edges in an image?

easy

A. Bilateral filter

B. Median filter

C. Gaussian filter

D. Box filter

Solution

1. Step 1: Understand salt-and-pepper noise characteristics

   Salt-and-pepper noise appears as random black and white pixels, which median filters handle well by replacing each pixel with the median of neighbors.

2. Step 2: Compare filters for edge preservation

   Median filters remove noise without blurring edges, unlike Gaussian filters which blur edges, and bilateral filters which are more complex but less effective for salt-and-pepper noise.

3. Final Answer:

   Median filter -> Option B

4. Quick Check:

   Salt-and-pepper noise = Median filter [OK]

Hint: Median filter excels at salt-and-pepper noise removal [OK]

Common Mistakes:

• Choosing Gaussian filter which blurs edges
• Confusing bilateral filter with median filter
• Using box filter which blurs noise and edges

2. Which of the following is the correct OpenCV function call to apply a Gaussian blur with a 5x5 kernel on an image stored in img?

easy

A. cv2.GaussianBlur(img, (5,5), 0)

B. cv2.medianBlur(img, 5)

C. cv2.blur(img, (5,5))

D. cv2.bilateralFilter(img, 5, 75, 75)

Solution

1. Step 1: Identify Gaussian blur function syntax

   OpenCV's GaussianBlur requires the image, kernel size as a tuple, and sigma (0 means auto).

2. Step 2: Match options to syntax

   cv2.GaussianBlur(img, (5,5), 0) matches the correct function and parameters for Gaussian blur with 5x5 kernel.

3. Final Answer:

   cv2.GaussianBlur(img, (5,5), 0) -> Option A

4. Quick Check:

   Gaussian blur syntax = cv2.GaussianBlur(img, (5,5), 0) [OK]

Hint: GaussianBlur uses tuple kernel size and sigma parameter [OK]

Common Mistakes:

• Using medianBlur for Gaussian blur
• Passing kernel size as single integer instead of tuple
• Confusing bilateralFilter parameters

3. What will be the effect of applying a bilateral filter with parameters d=9, sigmaColor=75, sigmaSpace=75 on a noisy image?

medium

A. Sharpens edges and increases noise

B. Blurs the entire image uniformly

C. Removes only salt-and-pepper noise

D. Smooths noise while preserving edges

Solution

1. Step 1: Understand bilateral filter parameters

   d controls neighborhood size; sigmaColor controls color similarity; sigmaSpace controls spatial closeness. Together they smooth noise but keep edges sharp.

2. Step 2: Analyze filter effect on noisy image

   Bilateral filter smooths noise while preserving edges, unlike Gaussian which blurs edges or median which targets salt-and-pepper noise.

3. Final Answer:

   Smooths noise while preserving edges -> Option D

4. Quick Check:

   Bilateral filter = noise smoothing + edge preservation [OK]

Hint: Bilateral filter smooths noise but keeps edges sharp [OK]

Common Mistakes:

• Thinking bilateral filter blurs edges
• Confusing bilateral with median filter
• Assuming it only removes salt-and-pepper noise

4. You applied a median filter with kernel size 3 on an image but the noise is still visible. What is the likely mistake?

medium

A. Median filter does not remove noise

B. Kernel size must be even number

C. Kernel size is too small to remove noise effectively

D. Median filter blurs edges too much

Solution

1. Step 1: Understand median filter kernel size effect

   Small kernel sizes may not cover enough pixels to remove noise effectively.

2. Step 2: Identify correct kernel size usage

   Median filter kernels must be odd-sized and larger kernels remove more noise but may blur details.

3. Final Answer:

   Kernel size is too small to remove noise effectively -> Option C

4. Quick Check:

   Increase kernel size for better noise removal [OK]

Hint: Use larger odd kernel sizes for stronger noise removal [OK]

Common Mistakes:

• Using even kernel sizes (invalid)
• Expecting median filter to blur edges heavily
• Thinking median filter can't remove noise

5. You want to denoise a photo with both Gaussian noise and preserve sharp edges of objects. Which sequence of filters should you apply for best results?

hard

A. Apply Gaussian blur first, then bilateral filter

B. Apply median filter first, then Gaussian blur

C. Apply bilateral filter first, then median filter

D. Apply median filter only

Solution

1. Step 1: Understand noise types and filter strengths

   Gaussian noise is best reduced by Gaussian blur; bilateral filter preserves edges while smoothing remaining noise.

2. Step 2: Determine filter order for best effect

   Applying Gaussian blur first reduces Gaussian noise; then bilateral filter smooths while preserving edges, improving overall quality.

3. Final Answer:

   Apply Gaussian blur first, then bilateral filter -> Option A

4. Quick Check:

   Gaussian blur + bilateral filter = noise reduction + edge preservation [OK]

Hint: Use Gaussian blur then bilateral filter for noise + edges [OK]

Common Mistakes:

• Applying median filter first which doesn't target Gaussian noise well
• Using only one filter for mixed noise
• Applying bilateral filter before Gaussian blur