https://github.com/auggen21/image-processing-basics-matlab-3

Inverse Filtering, Wiener Filter, Image Restoration, Hough Transform, Image segmentation using watershed, thresholding, region growing and region splitting and merging
https://github.com/auggen21/image-processing-basics-matlab-3

histogram-equalize hough-transform image-processing image-segmentation inverse-filtering matlab region-growing rgb-color-space-to-hsi split-and-merge-procedure thresholding watershed-transform wiener-filtering

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Inverse Filtering, Wiener Filter, Image Restoration, Hough Transform, Image segmentation using watershed, thresholding, region growing and region splitting and merging

• Host: GitHub
• URL: https://github.com/auggen21/image-processing-basics-matlab-3
• Owner: Auggen21
• Created: 2020-05-10T15:45:47.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-05-10T16:11:06.000Z (over 4 years ago)
• Last Synced: 2023-10-20T07:24:10.967Z (about 1 year ago)
• Topics: histogram-equalize, hough-transform, image-processing, image-segmentation, inverse-filtering, matlab, region-growing, rgb-color-space-to-hsi, split-and-merge-procedure, thresholding, watershed-transform, wiener-filtering
• Language: MATLAB
• Homepage:
• Size: 887 KB
• Stars: 13
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# image-processing-basics-matlab-3

 

1.	Write a MATLAB program which restores a degraded image using direct inverse filtering.

2.	Write a MATLAB function for implementing Wiener filtering for linear image restoration using (a) a constant ratio and (b) autocorrelation function.

3.	Write a MATLAB function to convert an RGB color space to HSI. Display the Hue image, Saturation image and the Intensity image. 

4.	Write a MATLAB function to histogram equalize the Intensity component of a color image and get a new HSI image. Convert the new HSI image back to RGB.

5.	Detect the line segments in a binary image using Hough Transform.

6.	Consider an image composed of small, non overlapping blobs. Segment the blobs based on thresholding.

7.	Consider an image composed of small, non overlapping blobs. Segment the blobs based on region growing.

8.	Implement the split and merge procedure for segmenting the image with different values for minimum dimensions of the quadtree regions.

9.	Consider a binary image composed of small blobs. Segment the blobs using (a) distance transform and (b) watershed transform.