SciPy - Image Processing (scipy.ndimage)
You have a 2D grayscale image with Gaussian noise. You want to smooth the image but preserve edges as much as possible. Which filter and parameters should you use?
You have a 2D grayscale image with Gaussian noise. You want to smooth the image but preserve edges as much as possible. Which filter and parameters should you use?
hard📝 Application Q8 of 15
Step-by-Step Solution
Solution:
Step 1: Understand noise type and filter effects
Gaussian noise is best reduced by smoothing filters, but uniform filters tend to blur edges. Median filters preserve edges better by using median statistics.Step 2: Evaluate options
Apply a median filter with size=3 and mode='nearest' uses median filter with a small window and 'nearest' mode, which preserves edges and reduces noise effectively.
Options B and C use uniform filters which blur edges.
Apply a median filter with size=7 and mode='wrap' uses a large median filter window which may oversmooth and mode 'wrap' can cause artifacts.Final Answer:
Apply a median filter with size=3 and mode='nearest' -> Option BQuick Check:
Median filter with small window preserves edges best [OK]
Quick Trick: Median filter preserves edges better than uniform filter [OK]
Common Mistakes:
- Using uniform filter for edge-preserving smoothing
- Choosing too large filter size causing oversmoothing
- Ignoring mode effects on border handling
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