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

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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

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

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