title: SSTV Image Enhancement description: Image Processing and Enhancement Technology
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SSTV Image Enhancement and Processing
Signal Processing Basics
FAQ
- Noise - Receiver noise and environmental interference
- Distortion - Frequency response issues
- Sync Failure - Clock or phase mismatch
- Color Error - Inaccurate colors
Pre-Processing Technologies
1. Frequency Response Compensation
Adjust the receiver's frequency response to compensate for nonlinearity in transmission and reception.
- Use equalizers
- Apply filters
- Calibrate audio devices
2. Automatic Gain Control (AGC)
Prevents receiver overload or underload.
- Set appropriate input levels
- Use AGC circuits
- Adjust manually if necessary
3. Noise Suppression
Reduce background noise.
- Spectral noise reduction
- Waveform smoothing
- Adaptive filtering
Post-Processing Enhancement
Denoising Algorithms
| Method | Advantages | Disadvantages |
|---|---|---|
| Gaussian Blur | Simple | Loss of details |
| Median Filter | Preserves edges | High computation |
| Bilateral Filter | Balanced | Complex |
| Wavelet | Multi-scale | Hard to adjust |
Contrast Enhancement
- Histogram Equalization - Global contrast
- Adaptive Histogram Equalization - Local enhancement
- Curve Adjustment - Manual control
- Color Balance - Fix color cast
Sharpening
Apply sharpening filters to enhance edges.
- Unsharp Mask (recommended)
- High-pass filter
- Directional sharpening
Color Correction
Color Restoration
- White Balance - Adjust overall color temperature
- Chrominance Correction - Fix chroma distortion
- Saturation Adjustment - Enhance colors
- Gamma Correction - Adjust brightness
Reference Image
Use a known good image as a reference for calibration.
Software Tools
Recommended Tools
- GIMP - Open-source image editing
- Photoshop - Professional editing
- ImageMagick - Command-line processing
- Python PIL/OpenCV - Programmatic processing
SSTV Specific Tools
- MMSSTV - Built-in enhancement
- EasyPal - User-friendly
- QSSTV - Linux specific
Best Practices
- Keep Originals - Save the original image before processing
- Stepwise Processing - Apply multiple small adjustments instead of big changes
- Compare Results - Compare before and after processing
- Use Appropriate Algorithms - Choose methods that match the image
- Avoid Overprocessing - Maintain a natural look
Automated Processing
Batch Processing
Apply the same processing to multiple images:
for image in *.jpg; do
convert $image -enhance -normalize -sharpen 1 enhanced_$image
done
Scripting
Automate complex processing workflows with Python or MATLAB.
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