aydnzn / Introduction-to-Image-Processing

A collection of assignments and a project completed for the course 'Introduction to Image Processing'

Introduction to Image Processing (Fall 2018-2019)

Welcome to the GitHub repository for the course Introduction to Image Processing. This repository serves as a central hub to showcase my solutions to homework assignments and term projects.

Course Summary

This course covers a broad range of topics related to digital image processing including image acquisition, camera technologies, image enhancement and restoration, image segmentation, edge detection, image compression, and coding. Furthermore, we delve into image recognition techniques with a focus on Deep Learning.

Repository Structure

This repository contains various assignments and a project, each housed in their respective folders. Here is a brief overview of each:

• ./Homework1: Engage with fundamental aspects of Image Processing and Analysis.

• ./Homework2: Gain hands-on experience with image histogram interpretation, quantization, enhancement, filtering, and comparison.

• ./Homework3: The aim here is to apply various image processing techniques such as smoothing, noise estimation, filtering on distinct types of noise and images. It also helps to comprehend image quality metrics and the utilization of heat maps in data visualization.

• ./Homework4: This assignment provides a deeper insight into frequency domain operations, unsharp masking, high-boost filtering, noise removal, and deblurring. It also highlights the significance of the phase spectrum in image reconstruction.

• ./Homework5: This focuses on the exploration and comparison of different edge detection methods, edge linking, and various image segmentation techniques, including region growing and clustering.

• ./Homework6: This assignment explores various morphological operations and aims to understand their impact on different image structures.

• ./Homework7 (Compression Lab): A practical examination of diverse compression algorithms and their implications on image quality.

• ./Project Cityscape Image Processing: This project utilizes the Place Pulse II dataset to create a binary classification task based on the safety score label. The repository comprises the project's code, model, and outcomes.