Image sharpening in Signal Processing - Mini Project: Build & Apply

Image sharpening

📖 Scenario: You have a blurry photo and want to make it clearer by sharpening the image. Sharpening helps to highlight edges and details, making the picture look crisper.

🎯 Goal: Build a simple image sharpening process using a sharpening filter on a small grayscale image represented as a 2D list of pixel values.

📋 What You'll Learn

Create a 2D list called image with exact pixel values

Create a sharpening filter matrix called sharpen_filter

Apply the sharpening filter to the image using convolution

Print the sharpened image as a 2D list

💡 Why This Matters

🌍 Real World

Image sharpening is used in photography and medical imaging to improve picture clarity and highlight important details.

💼 Career

Understanding image processing basics is useful for roles in computer vision, digital photography, and AI that work with visual data.

1

Create the original image data

Create a 3x3 2D list called image with these exact pixel values: [[100, 100, 100], [100, 50, 100], [100, 100, 100]]

Signal Processing

# Create the 3x3 image as a 2D list
# Your code here

Hint

Use square brackets to create a list of lists. Each inner list is a row of pixels.

2

Create the sharpening filter

Create a 3x3 sharpening filter called sharpen_filter with these exact values: [[0, -1, 0], [-1, 5, -1], [0, -1, 0]]

Signal Processing

image = [[100, 100, 100], [100, 50, 100], [100, 100, 100]]
# Create the sharpening filter as a 3x3 matrix
# Your code here

Hint

The sharpening filter has a strong center value and negative neighbors.

3

Apply the sharpening filter

Write code to apply the sharpen_filter to the image using convolution. Create a new 3x3 list called sharpened_image where each pixel is calculated by multiplying the filter with the corresponding pixels and summing the result. Ignore edges (keep them same as original). Use variables i and j to loop over the inner pixels.

Signal Processing

image = [[100, 100, 100], [100, 50, 100], [100, 100, 100]]
sharpen_filter = [[0, -1, 0], [-1, 5, -1], [0, -1, 0]]
# Apply the filter to the image and create sharpened_image
# Your code here

Hint

Use nested loops to slide the filter over the image center pixel only. Copy edges as is.

4

Print the sharpened image

Print the sharpened_image list to show the result.

Signal Processing

image = [[100, 100, 100], [100, 50, 100], [100, 100, 100]]
sharpen_filter = [[0, -1, 0], [-1, 5, -1], [0, -1, 0]]

sharpened_image = [row[:] for row in image]
for i in range(1, 2):
    for j in range(1, 2):
        val = 0
        for fi in range(3):
            for fj in range(3):
                val += image[i + fi - 1][j + fj - 1] * sharpen_filter[fi][fj]
        sharpened_image[i][j] = val
# Print the sharpened image
# Your code here

Hint

Use print(sharpened_image) to show the final sharpened image.

Practice

1. What is the main purpose of image sharpening in signal processing?

easy

A. To reduce the image size

B. To highlight edges and make the image clearer

C. To blur the image for artistic effect

D. To convert the image to grayscale

Solution

1. Step 1: Understand image sharpening

   Image sharpening enhances edges and details to make images clearer.

2. Step 2: Compare options

   Only To highlight edges and make the image clearer describes highlighting edges and clarity, which is the goal of sharpening.

3. Final Answer:

   To highlight edges and make the image clearer -> Option B

4. Quick Check:

   Image sharpening = highlight edges [OK]

Hint: Sharpening means making edges clearer, not blurring [OK]

Common Mistakes:

• Confusing sharpening with blurring
• Thinking sharpening changes image size
• Assuming sharpening converts color formats

2. Which of the following is the correct Python syntax to apply a simple sharpening kernel using convolution with OpenCV?

easy

A. cv2.sharpen(image, kernel)

B. cv2.convolve(image, kernel)

C. cv2.filter2D(image, -1, kernel)

D. cv2.applySharpen(image, kernel)

Solution

1. Step 1: Recall OpenCV function for convolution

   OpenCV uses cv2.filter2D() to apply kernels for filtering images.

2. Step 2: Check options

   Only cv2.filter2D(image, -1, kernel) uses cv2.filter2D with correct parameters; others are invalid functions.

3. Final Answer:

   cv2.filter2D(image, -1, kernel) -> Option C

4. Quick Check:

   OpenCV convolution = filter2D [OK]

Hint: Use cv2.filter2D for custom kernel convolution [OK]

Common Mistakes:

• Using non-existent OpenCV functions
• Confusing convolution with sharpening function names
• Incorrect parameter order

3. Given the following Python code using OpenCV, what will be the output type of sharpened?

import cv2
import numpy as np
image = cv2.imread('photo.jpg')
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
sharpened = cv2.filter2D(image, -1, kernel)

medium

A. A sharpened image as a NumPy array

B. An error due to kernel shape

C. A grayscale image

D. A blurred image

Solution

1. Step 1: Understand filter2D output

   cv2.filter2D returns an image as a NumPy array after applying the kernel.

2. Step 2: Analyze kernel and output

   The kernel is a sharpening kernel, so output is sharpened image, still in color and array form.

3. Final Answer:

   A sharpened image as a NumPy array -> Option A

4. Quick Check:

   filter2D output = NumPy array image [OK]

Hint: filter2D returns image array, not blurred or grayscale [OK]

Common Mistakes:

• Assuming output is blurred instead of sharpened
• Thinking output is grayscale without conversion
• Believing kernel shape causes error

4. Identify the error in this Python code snippet for image sharpening:

import cv2
import numpy as np
image = cv2.imread('img.png')
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
sharpened = cv2.filter2D(image, 1, kernel)
cv2.imshow('Sharpened', sharpened)
cv2.waitKey(0)
cv2.destroyAllWindows()

medium

A. The ddepth parameter should be -1, not 1

B. The kernel shape is invalid

C. cv2.imshow cannot display color images

D. cv2.waitKey is missing argument

Solution

1. Step 1: Check ddepth parameter in filter2D

   ddepth=-1 means output image has same depth as input; 1 is invalid and may cause errors.

2. Step 2: Validate other code parts

   Kernel shape is valid, cv2.imshow supports color, waitKey(0) is correct usage.

3. Final Answer:

   The ddepth parameter should be -1, not 1 -> Option A

4. Quick Check:

   ddepth=-1 for same depth [OK]

Hint: Use ddepth=-1 in filter2D to keep image depth [OK]

Common Mistakes:

• Using wrong ddepth value causing errors
• Thinking kernel shape is wrong
• Misunderstanding cv2.imshow capabilities

5. You want to sharpen an image using unsharp masking. Which sequence of steps correctly applies this method?

hard

A. Directly apply a sharpening kernel without blurring

B. Add noise to the image, then apply a sharpening kernel

C. Convert image to grayscale, then apply a sharpening kernel

D. Blur the image, subtract the blurred from original, then add result back to original

Solution

1. Step 1: Understand unsharp masking process

   Unsharp masking involves blurring the image, subtracting this blur from original to get edges, then adding edges back.

2. Step 2: Evaluate options

   Only Blur the image, subtract the blurred from original, then add result back to original correctly describes this sequence; others miss key steps or add unrelated actions.

3. Final Answer:

   Blur the image, subtract the blurred from original, then add result back to original -> Option D

4. Quick Check:

   Unsharp masking = blur, subtract, add [OK]

Hint: Unsharp masking = blur, subtract, add back edges [OK]

Common Mistakes:

• Skipping the blur step
• Adding noise instead of subtracting blur
• Applying sharpening kernel directly without unsharp steps