preprocessing-decisions

name	preprocessing-decisions
description	Filter selection decision tree, noise identification, edge detection priority, and kernel parameter rules
version	1.0.0
tags	["opencv","filtering","blur","edge-detection","noise","convolution"]

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

1. Gaussian Blur — Remove noise (you should also do this before calling Canny)
2. Sobel Gradients — Compute intensity gradients in X and Y
3. Gradient Magnitude & Direction — Find edge strength and angle
4. Non-Maximum Suppression — Thin edges to 1-pixel width
5. 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