Image Restoration
Image Restoration
Image Restoration is a scientific and mathematical process used to recover an image that has been damaged due to blur, noise, motion, or system errors. The main aim is not beauty, but truth — to get as close as possible to the original image.
Enhancement vs Restoration
| Aspect | Image Enhancement | Image Restoration |
|---|---|---|
| Focus | Visual improvement | Accuracy & recovery |
| Based on | Human perception | Mathematical models |
| Example | Increase brightness | Remove motion blur |
Real-Life Example
- Enhancement: Instagram filter
- Restoration: Police restoring CCTV footage for evidence
Image Degradation Model
Why Do Images Get Degraded?
Images get degraded due to:
- Camera shake
- Out-of-focus lens
- Poor lighting
- Transmission errors
- Dust, rain, fog
Mathematical Model
g(x, y) = f(x, y) ⊗ h(x, y) + η(x, y)
Meaning of Each Term
| Term | Detailed Meaning |
|---|---|
| f(x, y) | Perfect original scene |
| h(x, y) | How camera/system distorts image |
| η(x, y) | Random noise (unwanted data) |
| g(x, y) | Final observed image |
Simple Analogy
Imagine taking a photo through a dirty glass window:
- Scene = original image
- Dirty glass = degradation function
- Dust & scratches = noise
- Captured photo = degraded image
Properties of Image Restoration
Key Characteristics
| Property | Detailed Explanation |
|---|---|
| Model-based | Uses known degradation models |
| Deterministic | Tries to reverse damage logically |
| Noise-aware | Depends heavily on noise type |
| Objective | Minimum error reconstruction |
Important Exam Point: Restoration assumes prior knowledge of degradation.
Noise Models
Noise is any unwanted disturbance that hides or distorts useful information in an image.
Real-Life Sources of Noise
- Heat inside camera sensor
- Low-light photography
- Wireless image transmission
- Old scanners
Types of Noise
| Noise | Description | Real Example |
|---|---|---|
| Gaussian | Random variation | Smartphone night photo |
| Salt & Pepper | Black & white dots | Fax images |
| Poisson | Photon-based noise | Medical scans |
| Speckle | Multiplicative noise | Ultrasound images |
Mean Filters
Mean filter replaces each pixel with the average of surrounding pixels. It smoothens variations.
Real-Life Example: Like averaging opinions in a group — extreme values get neutralized.
Types of Mean Filters
| Filter | Explanation |
|---|---|
| Arithmetic | Simple average |
| Geometric | Reduces Gaussian noise |
| Harmonic | Removes salt noise |
| Contra-harmonic | Removes salt or pepper |
Disadvantage: Loss of sharp edges
Order Statistics Filters
Instead of averaging, these filters sort pixel values.
Median Filter
| Feature | Explanation |
|---|---|
| Uses middle value | Ignores extreme noise |
| Edge preserving | Does not blur boundaries |
Example: Removing white dots from scanned certificates.
Adaptive Filters
Why Adaptive?
Images are not uniform everywhere. Adaptive filters adjust themselves.
Behavior
- Flat area → more smoothing
- Edge area → less smoothing
Comparison
| Aspect | Normal Filter | Adaptive Filter |
|---|---|---|
| Fixed window | Yes | No |
| Intelligent | No | Yes |
Example: Mobile camera AI noise reduction.
Band Reject Filters
Removes specific frequency range containing noise.
Analogy: Noise-canceling headphones removing engine hum.
Application: Medical imaging, MRI noise removal.
Band Pass Filters
Allows only desired frequency band to pass.
Example: Highlighting edges in fingerprint recognition.
Notch Filters
Removes single-frequency periodic noise.
Example: Removing electrical interference lines from TV images.
Optimum Notch Filtering
Advanced Concept
Balances noise removal and image preservation.
Example: NASA satellite image correction.
Inverse Filtering
Divides degraded image by degradation function.
Limitation
Noise amplification when H(u,v) ≈ 0
Example: Deblurring shaky photos.
Wiener Filtering
Why Best?
Minimizes mean square error.
Considers
- Blur
- Noise
- Image statistics
Example: Medical X-ray enhancement.
Final Comparison Table
| Method | Best Use | Drawback |
|---|---|---|
| Mean | Low noise | Blurring |
| Median | Salt & pepper | Slower |
| Adaptive | Mixed noise | Complex |
| Inverse | Blur | Noise boost |
| Wiener | Blur + noise | Needs stats |
Memory Tricks
- Mean → Average
- Median → Middle
- Adaptive → Smart
- Notch → Specific
- Wiener → Winner filter