Image Processing Repository

Image Processing repository, contains a collection of exercises and projects completed as part of the Image Processing course.

Overview

Each directory in this repository corresponds to a specific exercise or project undertaken during the course. These exercises cover a wide range of topics within image processing, including but not limited to sampling, histograms, image enhancement, restoration, convolution, Fourier transforms, image compression, and advanced techniques like image blending and GAN inversions.

Contents

• Exercise_1/: Scene cut detection in videos.
• Exercise_2/: Audio denoising with histogram analysis.
• Exercise_3/: Image blending and creation of hybrid images.
• Exercise_4/: High-resolution image blending into low-resolution images.
• Exercise_5/: Deep learning-based image reconstruction using StyleGAN2.

Each directory includes:

• Python scripts implementing the exercise
• requirements.txt file listing necessary dependencies
• A detailed report in PDF format outlining the approach, algorithm, results, and insights
• Additional resources such as images, audio files, and Colab notebooks as applicable

Usage

To run any exercise, navigate to its corresponding directory and follow the specific instructions outlined in the README file located within that directory.

Getting Started

To get started with any project or exercise:

1. Clone the repository:

   git clone [repository-url]

2. Navigate to the specific project directory:

   cd [project-directory]

3. Install the required dependencies:

   pip install -r requirements.txt

4. Follow the README instructions within the project directory to run the scripts.