aeren23 Preprocessing Decisions
When to Use This Skill
- Choosing a blur/smoothing filter for noise reduction
- Selecting an edge detection algorithm
- Identifying noise type in an image
- Setting kernel sizes and filter parameters
- Building a preprocessing pipeline before segmentation or detection
Decision Framework
Filter Selection Decision Tree
What type of noise?
├── Salt & Pepper (random black/white dots)
│ └── ✅ Median Filter (cv2.medianBlur) — BEST IN THE WORLD for this
│
├── Gaussian noise (camera sensor heat, general grain)
│ └── ✅ Gaussian Blur (cv2.GaussianBlur)
│
├── Unknown noise + must preserve edges
│ └── ✅ Bilateral Filter (cv2.bilateralFilter) — kills noise, keeps edges
│
├── General smoothing (no specific noise type)
│ └── ✅ Mean Filter (cv2.blur) — simplest, fastest
│
└── Medical image with bias field / Rician noise
└── ✅ Non-Local Means (cv2.fastNlMeansDenoising)
Filter Comparison Matrix
| Filter | Speed | Edge Preservation | Noise Removal | Best For |
|---|---|---|---|---|
Mean (cv2.blur) |
⚡⚡⚡ | ❌ Poor | ⭐⭐ | General smoothing |
Gaussian (cv2.GaussianBlur) |
⚡⚡⚡ | ⭐ Fair | ⭐⭐⭐ | Gaussian noise, pre-Canny |
Median (cv2.medianBlur) |
⚡⚡ | ⭐⭐ Good | ⭐⭐⭐⭐⭐ (S&P) | Salt & Pepper noise |
Bilateral (cv2.bilateralFilter) |
⚡ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | Edge-aware denoising |
Rule of thumb: If you don't know the noise type, start with Gaussian. If edges matter, use Bilateral. If you see random black/white dots, use Median — nothing else comes close.
Edge Detection Priority
What do you need to detect?
├── General edges (most use cases)
│ └── ✅ Canny (cv2.Canny) — gold standard, 5-step pipeline
│
├── Directional edges (horizontal OR vertical)
│ └── ✅ Sobel (cv2.Sobel) — first derivative, specify dx/dy
│
├── Fine detail + corners + all boundaries
│ └── ✅ Laplacian (cv2.Laplacian) — second derivative, zero-crossing
│
└── Text/document character edges (OCR preprocessing)
└── ✅ Prewitt — better than Sobel for text sharpness
Edge Detection Comparison
| Detector | Derivative | Output | Strengths | Weaknesses |
|---|---|---|---|---|
| Sobel | 1st | Directional gradient map | Clean directional edges | Misses some corners |
| Prewitt | 1st | Similar to Sobel | Better for text/documents | Noisier than Sobel |
| Laplacian | 2nd | All edges via zero-crossing | Catches finest details | Very noise-sensitive |
| Canny | Multi-step | Thin binary edges | Best general-purpose | Sensitive to parameters |
Critical Gotchas
1. Gaussian Blur Before Canny — MANDATORY
Canny is extremely noise-sensitive. Without pre-blurring, it will detect noise as edges.
# WRONG — will produce noisy edges
edges = cv2.Canny(img, 100, 200)
# CORRECT — always blur first
blurred = cv2.GaussianBlur(img, (5, 5), 0)
edges = cv2.Canny(blurred, 100, 200)
2. Kernel Size Must ALWAYS Be Odd
Every kernel/filter size in OpenCV must be an odd number (3, 5, 7, 9...). Even numbers will crash.
# CRASHES
cv2.GaussianBlur(img, (4, 4), 0) # Error!
# CORRECT
cv2.GaussianBlur(img, (5, 5), 0)
3. Bilateral Filter Is Slow
Bilateral preserves edges beautifully but is significantly slower than other filters. For real-time video, prefer Gaussian or Median.
# Bilateral parameters: (src, diameter, sigmaColor, sigmaSpace)
cv2.bilateralFilter(img, 9, 75, 75)
# diameter=9: neighborhood size
# sigmaColor=75: color range for blending
# sigmaSpace=75: spatial distance for blending
4. Median Filter Kernel Must Be a Single Odd Integer
Unlike other filters that take a tuple (5, 5), median takes just one integer:
# WRONG
cv2.medianBlur(img, (5, 5)) # Error!
# CORRECT
cv2.medianBlur(img, 5)
5. The ddepth=-1 Convention
In filter functions, ddepth=-1 means "output same depth as input." For float precision:
cv2.filter2D(img, -1, kernel) # Output = same type as input
cv2.filter2D(img, cv2.CV_64F, kernel) # Output = 64-bit float
Quick Reference
Convolution Basics
- Kernel (Mask): Small matrix (3×3, 5×5) slid over the image
- Convolution: Multiply kernel × image patch, sum results, write to center pixel
- Padding: Add zero-pixels around borders so kernel can process edge pixels
- Larger kernel = stronger effect but slower and may lose detail
Frequency Domain Concepts
| Frequency | Visual Appearance | Examples |
|---|---|---|
| Low frequency | Smooth, gradual changes | Background, skin, sky |
| High frequency | Sharp, sudden changes | Edges, textures, noise |
- Low-pass filters (blur) remove high frequency → smooth image
- High-pass filters (sharpen) emphasize high frequency → enhance edges
- Sharpening kernel example: Center = high positive (e.g., 9), neighbors = negative (e.g., -1)
Noise Type Identification
| Noise Type | Visual Pattern | Cause | Best Filter |
|---|---|---|---|
| Salt & Pepper | Random pure white and pure black pixels | Sensor errors, transmission | Median |
| Gaussian | Uniform grain/static across image | Sensor heat, low light | Gaussian Blur |
| Speckle | Multiplicative granular noise | Ultrasound, SAR radar | Bilateral |
| MRI Bias Field | Smooth intensity variation across image | B0 field inhomogeneity | Non-Local Means |
Canny Edge Detection — The 5 Steps
- Gaussian Blur — Remove noise (you should also do this before calling Canny)
- Sobel Gradients — Compute intensity gradients in X and Y
- Gradient Magnitude & Direction — Find edge strength and angle
- Non-Maximum Suppression — Thin edges to 1-pixel width
- Hysteresis Thresholding — Connect edges using upper/lower thresholds
# threshold1 = lower bound, threshold2 = upper bound
# Ratio recommendation: 1:2 or 1:3
edges = cv2.Canny(blurred, 50, 150) # 1:3 ratio