This pipeline automatically cleans and enhances scanned historical newspaper images by reducing noise, improving contrast, and sharpening text for better readability.

Features

Noise Reduction: Bilateral filtering and non-local means denoising
Contrast Enhancement: CLAHE and gamma correction
Background Cleaning: Morphological operations to remove artifacts
Text Sharpening: Unsharp masking for improved readability
Batch Processing: Process entire directories efficiently
Interactive Tuning: Find optimal parameters for your specific images
Before/After Comparisons: Visual validation of improvements

Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Process Single Image

python image_cleaner.py input_image.jpg -o cleaned_image.jpg --comparison

3. Batch Process Directory

python batch_process.py -i newspaper_scans -o cleaned_images

4. Interactive Parameter Tuning

python config_tuner.py sample_image.jpg

Usage Examples

Basic Image Cleaning

# Clean single image with default settings
python image_cleaner.py 1771-09b-02.jpg

# Clean with specific processing steps
python image_cleaner.py 1771-09b-02.jpg --steps denoise contrast sharpen

# Create before/after comparison
python image_cleaner.py 1771-09b-02.jpg -c

Batch Processing

# Process all JPG files in current directory
python batch_process.py

# Process specific directory with custom output
python batch_process.py -i scans/ -o cleaned/

# Use custom configuration
python batch_process.py --config custom_config.json

# Skip comparison images for faster processing
python batch_process.py --no-comparisons

Parameter Tuning

# Start interactive tuning session
python config_tuner.py sample_image.jpg

# Load existing config for fine-tuning
python config_tuner.py sample_image.jpg -c existing_config.json

Configuration

Default Parameters

The pipeline uses these default parameters optimized for newspaper scans:

{
    "bilateral_d": 9,
    "bilateral_sigma_color": 75,
    "bilateral_sigma_space": 75,
    "clahe_clip_limit": 2.0,
    "clahe_grid_size": [8, 8],
    "gamma": 1.2,
    "denoise_h": 10,
    "morph_kernel_size": 2,
    "unsharp_amount": 1.5,
    "unsharp_radius": 1.0,
    "unsharp_threshold": 0
}

Parameter Descriptions

bilateral_d: Neighborhood diameter for bilateral filtering (5-15)
bilateral_sigma_color: Color space filter strength (50-150)
bilateral_sigma_space: Coordinate space filter strength (50-150)
clahe_clip_limit: Contrast limiting for CLAHE (1.0-4.0)
clahe_grid_size: CLAHE tile grid size [width, height] (4-16)
gamma: Gamma correction value (0.8-2.0)
denoise_h: Denoising filter strength (5-20)
morph_kernel_size: Morphological operation kernel size (1-5)
unsharp_amount: Unsharp masking strength (0.5-3.0)
unsharp_radius: Unsharp masking radius (0.5-2.0)
unsharp_threshold: Unsharp masking threshold (0-10)

Creating Custom Configurations

Generate default config template:

python batch_process.py --create-config

Edit config.json with your preferred values
Use custom config:

python batch_process.py --config config.json

Processing Pipeline

The image cleaning pipeline applies these steps in sequence:

Noise Reduction
- Bilateral filtering preserves edges while reducing noise
- Non-local means denoising removes repetitive patterns
Contrast Enhancement
- CLAHE improves local contrast adaptively
- Gamma correction adjusts overall brightness
Background Cleaning
- Morphological operations remove small artifacts
- Background normalization reduces paper texture
Sharpening
- Unsharp masking enhances text edges
- Preserves fine details while reducing blur

Interactive Tuning Commands

When using config_tuner.py, these commands are available:

set <param> <value> - Adjust parameter value
show - Display current parameters
test [steps] - Process with current settings
compare [filename] - Save before/after comparison
save <filename> - Save configuration to file
load <filename> - Load configuration from file
presets - Show preset configurations
help - Show detailed help
quit - Exit tuning session

Tips for Best Results

For Light Damage/Noise:

Reduce bilateral_d to 5-7
Lower denoise_h to 5-8
Use clahe_clip_limit around 1.5

For Heavy Damage/Artifacts:

Increase bilateral_d to 12-15
Raise denoise_h to 15-20
Use higher clahe_clip_limit (3.0-4.0)

For Faded/Low Contrast Images:

Increase gamma to 1.3-1.5
Raise clahe_clip_limit to 3.0+
Boost unsharp_amount to 2.0+

For Sharp/High Quality Scans:

Focus mainly on denoise and sharpen steps
Skip background cleaning if unnecessary
Use lighter settings to preserve quality

File Structure

newspaper_image_cleaner/
├── image_cleaner.py      # Core processing module
├── batch_process.py      # Batch processing script
├── config_tuner.py       # Interactive parameter tuning
├── requirements.txt      # Python dependencies
└── README.md            # This documentation

Troubleshooting

ImportError: No module named 'cv2'

Install OpenCV: pip install opencv-python

Memory Issues with Large Images

The tuner automatically resizes large images. For batch processing of very large images, consider resizing first.

Poor Results

Use the interactive tuner to find optimal parameters for your specific image characteristics.

Performance

Single 3000x2000 image: ~3-5 seconds
Batch processing depends on image size and quantity
Interactive tuning uses smaller images for faster feedback