https://github.com/fahimahammed/digital-image-processing

CSE4182 Digital Image Processing Lab. Code for resolution modifications, histogram analysis, brightness enhancement, noise suppression, and frequency domain operations. Ideal for learning image processing.
https://github.com/fahimahammed/digital-image-processing

image-processing ipynb jupiter-notebook python3

Last synced: 3 days ago
JSON representation

CSE4182 Digital Image Processing Lab. Code for resolution modifications, histogram analysis, brightness enhancement, noise suppression, and frequency domain operations. Ideal for learning image processing.

• Host: GitHub
• URL: https://github.com/fahimahammed/digital-image-processing
• Owner: fahimahammed
• Created: 2023-07-15T20:44:11.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-21T12:22:16.000Z (10 months ago)
• Last Synced: 2024-11-13T08:41:22.040Z (2 months ago)
• Topics: image-processing, ipynb, jupiter-notebook, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 19.9 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Digital Image Processing Lab

### Course Information:

| **Course Code** | **Course Title**             |

| --------------- | ---------------------------- |

| CSE4182         | Digital Image Processing Lab |

## Overview

This repository contains the lab tasks for the Digital Image Processing course (CSE4182). The tasks are designed to provide hands-on experience in various image processing techniques using Python and relevant libraries.

### Lab Tasks

- **Task 1: Take the grayscale image of size 512x512 & perform the following operations -**

	- (a) Decrease its spatial resolution by half every time & observe its change when displaying in the same window size

	- (b) Decrease its intensity level resolution by one bit up to reach its binary format & observe its change when displaying in the same window size

	- (c) Illustrate the histogram of the image & make single threshold segmentation observed from the histogram

- **Task 2: Take a grayscale image of size 512x512 & perform the following operations –**

	- (a) Perform the brightness enhancement of a specific range of gray levels & observe its result

	- (b) Differentiate the results of power law & inverse logarithmic transformation 

	- (c) Find the difference image between original & the image obtained by last three MSBs

- **Task 3: Take a grayscale image of size 512x512, add some salt-and-pepper noise & perform the following operations –**

	- (a) Apply average & median spatial filters with 5x5 mask & observe their performance for noise suppression in term of PSNR 

	- (b) Apply average filter with (3x3, 5x5, 7x7) mask with average filter & observe their performance in term of PSNR 

	- (c) Apply harmonic & geometric mean filter on the noisy image & compare their performance with PSNR 

- **Task 4: Take a grayscale image of size 512x512, add some Gaussian noise & perform the following operations in the frequency domain –**

	- (a) Apply 4th order Butterworth & Gaussian low-pass filter to analyze their performance quantitatively 

	- (b) Display the ringing effect of the ideal low-pass filter of different radius on the image 

	- (c) Perform edge detection of given the noisy & clean image using ideal & Gaussian high-pass filters 

 - **Task 5: Take a binary image & perform the following morphological operations –**

	- (a) Perform Erosion & Dilation operations

	- (b) Opening & Closing operations 

	- (c) Boundary extraction using morphological operation