When processing a noisy grayscale image with SciPy, which approach best enhances edge detection using Sobel and Laplace filters?

hard📝 Application Q8 of 15

SciPy - Image Processing (scipy.ndimage)

AApply Gaussian smoothing, then Sobel for gradient, followed by Laplace for edge refinement

BDirectly apply Laplace filter without smoothing, then Sobel for edge detection

CUse Sobel filter twice along the same axis without smoothing

DApply Laplace filter first, then Gaussian smoothing to reduce noise

Step-by-Step Solution

Solution:

Step 1: Noise Reduction
Applying Gaussian smoothing reduces noise, which improves edge detection accuracy.
Step 2: Edge Gradient Detection
Sobel filter detects gradients (edges) effectively after smoothing.
Step 3: Edge Refinement
Applying Laplace filter after Sobel helps refine edges by detecting zero-crossings.
Final Answer:
Apply Gaussian smoothing, then Sobel for gradient, followed by Laplace for edge refinement -> Option A
Quick Check:
Smooth first, then Sobel, then Laplace for best edge detection [OK]

Quick Trick: Smooth noisy image first, then Sobel, then Laplace [OK]

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