https://github.com/sohomm/imageenhancementformissions

Image Enhancement Digital Image Processing Computer Vision Deep Learning-based Image Restoration Convolutional Neural Networks (CNN) Super-Resolution Techniques Noise Reduction and Denoising Image Quality Improvement Python Programming TensorFlow / PyTorch (whichever you used) OpenCV & NumPy Evaluation Metrics: PSNR, SSIM Model Training
https://github.com/sohomm/imageenhancementformissions

cnn computer-vision deep-learning drdo enhancement image-processing isro opencv

Last synced: 7 months ago
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Image Enhancement Digital Image Processing Computer Vision Deep Learning-based Image Restoration Convolutional Neural Networks (CNN) Super-Resolution Techniques Noise Reduction and Denoising Image Quality Improvement Python Programming TensorFlow / PyTorch (whichever you used) OpenCV & NumPy Evaluation Metrics: PSNR, SSIM Model Training

• Host: GitHub
• URL: https://github.com/sohomm/imageenhancementformissions
• Owner: SohomM
• Created: 2025-05-16T19:41:23.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-05-16T20:27:58.000Z (8 months ago)
• Last Synced: 2025-06-06T01:24:53.006Z (7 months ago)
• Topics: cnn, computer-vision, deep-learning, drdo, enhancement, image-processing, isro, opencv
• Language: Jupyter Notebook
• Homepage:
• Size: 1020 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Image Enhancement for Mission-Critical Applications

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.

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## Purpose

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

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## How the Code Works

1. **Load Raw Images:** Images captured from cameras or sensors are loaded from the dataset folder.

2. **Preprocessing:** The images are resized, normalized, and prepared for model input to ensure consistent processing.

3. **Image Enhancement Model:** A convolutional neural network (CNN) or other trained model is applied to remove noise, sharpen edges, and improve contrast.

4. **Postprocessing:** The enhanced output is converted back to standard image format and saved in the results folder.

5. **Evaluation:** Quantitative metrics such as PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) are calculated to measure enhancement quality.

6. **Visualization:** Side-by-side comparisons help verify improvements and support further analysis.

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## Mission Impact

- **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.

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## Getting Started

Run the Python script to process images in your dataset and generate enhanced outputs:

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## visit my portfolio

www.sohommukherjee.wordpress.com