How LightBoost Works
Discover the advanced AI technology behind our low-light image enhancement system. From intelligent analysis to machine learning-powered enhancement, learn how we transform your dark photos into stunning images.
Complete Workflow Overview
Image Input
Upload images or capture with camera
AI Analysis
Analyze lighting conditions and image properties
Validation
Determine if enhancement is needed
Enhancement
Apply AI-powered enhancement algorithms
Upscaling (Optional)
Increase resolution with AI upscaling
Output
Download or save enhanced image
Data Flow Visualization
Raw Image
AI Analysis
Enhancement
Enhanced Output
Step-by-Step Process
Image Upload
Upload your low-light images or capture new photos using your device camera
AI Analysis
Our AI analyzes your image to determine lighting conditions and enhancement potential
Validation
System validates whether your image would benefit from low-light enhancement
Enhancement
Machine learning models enhance your image with detail recovery
Download
Download your enhanced image
Intelligent Image Analysis
Categorizes overall lighting as very dark, dark, moderate, bright, or very bright
Example: Dark indoor scene → 'dark'
Measures overall image brightness on a scale from 0 (black) to 255 (white)
Example: Low-light photo → 45.2
Calculates percentage of pixels below brightness threshold (typically 50)
Example: Night photo → 78.5%
Evaluates the difference between light and dark areas in the image
Example: Low contrast → 0.23
Image Preprocessing
Convert image to appropriate format and normalize pixel values for analysis
Statistical Analysis
Calculate brightness histogram, contrast metrics, and pixel distribution statistics
Classification
Categorize lighting conditions and determine enhancement suitability
Lighting Condition
Must be 'very_dark' or 'dark'
Well-lit images don't benefit from low-light enhancement
Dark Pixel Percentage
Must be ≥ 3.0%
Images with too few dark pixels are already well-exposed
Combined Analysis
Both conditions must be met
Ensures enhancement is only applied where it will improve quality
✓ Enhancement Recommended
✗ Enhancement Not Needed
AI-Powered Enhancement
Machine learning models enhance hidden details in shadows and dark regions
Techniques Used:
- Shadow lifting
- Histogram equalization
- Adaptive contrast enhancement
Intelligent color balancing improves vibrancy while maintaining natural appearance
Techniques Used:
- White balance correction
- Saturation enhancement
- Tone mapping
Neural networks increase resolution while adding realistic details
Techniques Used:
- Super-resolution CNN
- Edge enhancement
- Texture synthesis
Input Image
Color Correction
Detail Enhancement
Upscaling (Optional)
Final Output
Detail Enhancement
Resolution Increase
Machine Learning Models
Low-light image enhancement and color correction
Architecture
Encoder-decoder with skip connections and attention mechanisms
Input/Output
In: Low-light images (variable resolution)
Out: Enhanced images with improved brightness and reduced noise
Increase image resolution while preserving details
Architecture
Generator-discriminator network with perceptual loss
Input/Output
In: Enhanced images at original resolution
Out: High-resolution images (2x-4x scale factor)
Analysis Model
Evaluates image quality and determines enhancement suitability
Enhancement Model
Applies AI-powered enhancement to improve image quality
Upscaling Model
Optionally increases resolution with detail preservation
Training Dataset
- • 6K+ low-light images by ExDark Dataset
- • Without ground truth images
- • Diverse lighting conditions and scenes
- • Photography shoot
Performance Metrics
Technical Implementation
- Next.js 15 with App Router
- React 19 with TypeScript
- Tailwind CSS for styling
- Real-time image processing
- Python Flask API server
- TensorFlow models
- RESTful API endpoints
- Scalable cloud deployment
- ~2.5s average processing time
- GPU-accelerated inference
- Optimized model architectures
- Efficient memory usage
- Client-side image storage
- No server-side data retention
- Secure API communication
- Privacy-first design
/api/healthCheck API service status
Returns: Service health information
/api/analyzeAnalyze image lighting conditions
Returns: Lighting metrics and enhancement recommendation
/api/enhance/lowlightEnhance low-light images
Returns: Enhanced image data (PNG format)
/api/enhance/upscaleEnhance and upscale images
Returns: Enhanced and upscaled image data
Frontend
Next.js application with real-time image processing interface
API Server
Python Flask server hosting machine learning models
ML Models
TensorFlow/PyTorch models for analysis and enhancement
Average Processing Time
Maximum File Size
Uptime Reliability
Maximum Resolution
Ready to Try It Yourself?
Now that you understand how our technology works, experience the power of AI-enhanced low-light photography for yourself.