You have a noisy grayscale image stored as a 2D NumPy array. Which sequence of SciPy ndimage transforms would best prepare it for edge detection?

hard📝 Application Q15 of 15

SciPy - Image Processing (scipy.ndimage)

AApply a median filter to increase noise, then blur the image

BApply Gaussian smoothing to reduce noise, then use a Sobel filter to detect edges

CDirectly apply edge detection without smoothing

DInvert the image colors before any filtering

Step-by-Step Solution

Solution:

Step 1: Understand noise reduction before edge detection
Reducing noise with Gaussian smoothing helps avoid false edges.
Step 2: Apply edge detection after smoothing
Sobel filter detects edges effectively after noise is reduced.
Final Answer:
Apply Gaussian smoothing to reduce noise, then use a Sobel filter to detect edges -> Option B
Quick Check:
Smooth then detect edges = Apply Gaussian smoothing to reduce noise, then use a Sobel filter to detect edges [OK]

Quick Trick: Smooth noisy image before edge detection for best results [OK]

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