MATLAB → Python → PyTorch pipeline for image processing, denoising, and super-resolution.

Overview

This project presents an end-to-end workflow that progresses from classical image processing techniques in MATLAB to Python-based numerical analysis and finally to deep learning–based image super-resolution using PyTorch. The project emphasizes reproducibility, cross-platform validation, and engineering reasoning through quantitative performance analysis rather than treating deep learning as a black-box solution.

Features

Classical Image Processing in MATLAB

• Image normalization
• Histogram analysis
• Noise modeling
• Gaussian filtering
• Bilateral filtering
• Contrast enhancement
• Edge detection
• Image quality evaluation

Python Reimplementation and Validation

• Translation of MATLAB workflows into Python
• Comparison of numerical differences between platforms
• Parameter sensitivity analysis
• Quantitative evaluation using standard metrics

Deep Learning Super-Resolution

• Generation of paired low-resolution and high-resolution datasets
• Implementation of SRCNN (Super-Resolution Convolutional Neural Network)
• Model training using PyTorch
• Evaluation using PSNR and SSIM
• Visual comparison of input, output, and reference images

Programming Languages

• MATLAB
• Python 3

Libraries and Frameworks

• PyTorch
• NumPy
• SciPy
• OpenCV
• scikit-image
• Matplotlib

Technical Areas

• Signal Processing
• Image Processing
• Image Enhancement
• Denoising
• Convolutional Neural Networks
• Super-Resolution
• Performance Evaluation

Project Structure

project_root/ │ ├── data/ │ ├── matlab_pipeline/ │ ├── main_processing.m │ └── results/ │ ├── python_pipeline/ │ ├── prepare_data.py │ ├── denoise.py │ ├── enhance.py │ ├── evaluate.py │ └── results/ │ ├── deep_learning/ │ ├── srcnn.py │ ├── dataset.py │ ├── train.py │ ├── evaluate.py │ ├── inference.py │ └── results/ │ └── docs/

Evaluation Metrics

The following metrics are used throughout the project:

• Mean Squared Error (MSE)
• Peak Signal-to-Noise Ratio (PSNR)
• Structural Similarity Index (SSIM)

Key Engineering Insights

• Numerical results vary across software platforms due to implementation differences.
• Parameter tuning significantly affects image quality metrics.
• Classical image processing techniques may outperform naïve deep learning models in some cases.
• Understanding the reasons behind numerical differences is as important as improving benchmark scores.

Future Work

Possible future extensions include:

• Enhanced Deep Super-Resolution (EDSR)
• ESRGAN
• Video Super-Resolution
• Real-time image enhancement
• Core ML deployment for Apple devices

Author

UMER UMER MOHAMMED

• GitHub: @engr-umer-mohammed
• LinkedIn: @engr-umer-mohammed
• Email: umermohammed62@gmail.com

This project was developed as a portfolio project to demonstrate engineering reasoning, reproducibility, and practical experience in image processing and deep learning.

License

MIT

Acknowledgments

[salimlemma@gmail.com]