{"id":14977213,"url":"https://github.com/fahimahammed/digital-image-processing","last_synced_at":"2026-03-06T13:05:25.547Z","repository":{"id":224737336,"uuid":"666862575","full_name":"fahimahammed/digital-image-processing","owner":"fahimahammed","description":"CSE4182 Digital Image Processing Lab. Code for resolution modifications, histogram analysis, brightness enhancement, noise suppression, and frequency domain operations. 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The tasks are designed to provide hands-on experience in various image processing techniques using Python and relevant libraries.\n\n### Lab Tasks\n- **Task 1: Take the grayscale image of size 512x512 \u0026 perform the following operations -**\n\t- (a) Decrease its spatial resolution by half every time \u0026 observe its change when displaying in the same window size\n\t- (b) Decrease its intensity level resolution by one bit up to reach its binary format \u0026 observe its change when displaying in the same window size\n\t- (c) Illustrate the histogram of the image \u0026 make single threshold segmentation observed from the histogram\n- **Task 2: Take a grayscale image of size 512x512 \u0026 perform the following operations –**\n\t- (a) Perform the brightness enhancement of a specific range of gray levels \u0026 observe its result\n\t- (b) Differentiate the results of power law \u0026 inverse logarithmic transformation \n\t- (c) Find the difference image between original \u0026 the image obtained by last three MSBs\n- **Task 3: Take a grayscale image of size 512x512, add some salt-and-pepper noise \u0026 perform the following operations –**\n\t- (a) Apply average \u0026 median spatial filters with 5x5 mask \u0026 observe their performance for noise suppression in term of PSNR \n\t- (b) Apply average filter with (3x3, 5x5, 7x7) mask with average filter \u0026 observe their performance in term of PSNR \n\t- (c) Apply harmonic \u0026 geometric mean filter on the noisy image \u0026 compare their performance with PSNR \n- **Task 4: Take a grayscale image of size 512x512, add some Gaussian noise \u0026 perform the following operations in the frequency domain –**\n\t- (a) Apply 4th order Butterworth \u0026 Gaussian low-pass filter to analyze their performance quantitatively \n\t- (b) Display the ringing effect of the ideal low-pass filter of different radius on the image \n\t- (c) Perform edge detection of given the noisy \u0026 clean image using ideal \u0026 Gaussian high-pass filters \n - **Task 5: Take a binary image \u0026 perform the following morphological operations –**\n\t- (a) Perform Erosion \u0026 Dilation operations\n\t- (b) Opening \u0026 Closing operations \n\t- (c) Boundary extraction using morphological operation\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffahimahammed%2Fdigital-image-processing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffahimahammed%2Fdigital-image-processing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffahimahammed%2Fdigital-image-processing/lists"}