This Python project enhances images to improve clarity and detail, crucial for defense, surveillance, and biometric identification missions. Using advanced image processing and deep learning techniques, it transforms raw, noisy images into clear, actionable visuals.
In high-stakes environments such as military operations and security systems, image quality can directly impact decision-making and operational success. Enhanced images help in:
- Detecting threats in surveillance footage
- Clear image for defence missions
- Improving accuracy of facial recognition and biometric systems
- Analyzing thermal or night-vision imagery with higher precision
- Load Raw Images: Images captured from cameras or sensors are loaded from the dataset folder.
- Preprocessing: The images are resized, normalized, and prepared for model input to ensure consistent processing.
- Image Enhancement Model: A convolutional neural network (CNN) or other trained model is applied to remove noise, sharpen edges, and improve contrast.
- Postprocessing: The enhanced output is converted back to standard image format and saved in the results folder.
- Evaluation: Quantitative metrics such as PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) are calculated to measure enhancement quality.
- Visualization: Side-by-side comparisons help verify improvements and support further analysis.
- Defense: Enables clearer reconnaissance and surveillance images, enhancing situational awareness.
- Identification: Improves biometric and forensic image accuracy for reliable recognition and verification.
- Security: Supports real-time monitoring systems by providing sharper visuals under poor lighting or adverse conditions.
Run the Python script to process images in your dataset and generate enhanced outputs: