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This project showcases a variety of digital image processing techniques implemented in MATLAB. It includes graphical user interfaces (GUIs) for tasks such as bit gray slicing, filtering, histogram equalization, and more. Designed for both educational and practical applications, it aims to enhance understanding of image processing concepts.

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Digital Image Processing Project

This project demonstrates various digital image processing techniques implemented in MATLAB.

Project Files

  • BitGraySlicing.fig - GUI for Bit Gray Slicing
  • BitGraySlicing.m - MATLAB script for Bit Gray Slicing
  • Gaussian_high_pass_filter.fig - GUI for Gaussian High Pass Filter
  • Gaussian_high_pass_filter.m - MATLAB script for Gaussian High Pass Filter
  • Gaussian_low_pass_filter.fig - GUI for Gaussian Low Pass Filter
  • Gaussian_low_pass_filter.m - MATLAB script for Gaussian Low Pass Filter
  • Histogram.fig - GUI for Histogram processing
  • Histogram.m - MATLAB script for Histogram processing
  • IdealHighpassFilterfig.fig - GUI for Ideal High Pass Filter
  • IdealHighpassFilterfig.m - MATLAB script for Ideal High Pass Filter
  • Log.fig - GUI for Log Transform
  • Log.m - MATLAB script for Log Transform
  • Negative_img.fig - GUI for Negative Image transformation
  • Negative_img.m - MATLAB script for Negative Image transformation
  • Power_Low.fig - GUI for Power Low transformation
  • Power_Low.m - MATLAB script for Power Low transformation
  • add.fig - GUI for Image Addition
  • add.m - MATLAB script for Image Addition
  • butterworthLowPassFilter.fig - GUI for Butterworth Low Pass Filter
  • butterworthLowPassFilter.m - MATLAB script for Butterworth Low Pass Filter
  • butterworthhighpassfilter.fig - GUI for Butterworth High Pass Filter
  • butterworthhighpassfilter.m - MATLAB script for Butterworth High Pass Filter
  • composite_laplacian.fig - GUI for Composite Laplacian filter
  • composite_laplacian.m - MATLAB script for Composite Laplacian filter
  • cotrast.fig - GUI for Contrast adjustment
  • cotrast.m - MATLAB script for Contrast adjustment
  • firstapp.fig - GUI for First App
  • firstapp.m - MATLAB script for First App
  • grayLevel_2ndApproch.fig - GUI for Gray Level 2nd Approach
  • grayLevel_2ndApproch.m - MATLAB script for Gray Level 2nd Approach
  • greylevel.fig - GUI for Gray Level Processing
  • greylevel.m - MATLAB script for Gray Level Processing
  • hisogram_E.fig - GUI for Histogram Equalization
  • hisogram_E.m - MATLAB script for Histogram Equalization
  • ideallowpassfilter.fig - GUI for Ideal Low Pass Filter
  • ideallowpassfilter.m - MATLAB script for Ideal Low Pass Filter
  • project.fig - Main project GUI
  • project.m - Main project script
  • resamplingupp.fig - GUI for Image Resampling (Upsampling)
  • resamplingupp.m - MATLAB script for Image Resampling (Upsampling)
  • sec.fig - GUI for secondary functionalities
  • sec.m - MATLAB script for secondary functionalities
  • secondApproch_grayLevelSlicing.m - MATLAB script for Second Approach to Gray Level Slicing
  • subtract_num_from_imgfig.fig - GUI for Subtracting Numbers from Image
  • subtract_num_from_imgfig.m - MATLAB script for Subtracting Numbers from Image
  • thresholdapp.fig - GUI for Thresholding application
  • thresholdapp.m - MATLAB script for Thresholding application

How to Run This Project in MATLAB

  1. Ensure you have MATLAB installed on your computer.
  2. Download or clone this repository to your local machine.
  3. Open MATLAB and navigate to the directory where the project files are located.
  4. In the MATLAB Command Window, you can open any GUI by typing its corresponding command. For example, to open the Bit Gray Slicing GUI, type:
  5. >> BitGraySlicing
  6. Follow the instructions provided in the GUI to perform the desired image processing tasks.
  7. For running any script, type its name without the .m extension, for example:
  8. >> Gaussian_high_pass_filter

About

This project showcases a variety of digital image processing techniques implemented in MATLAB. It includes graphical user interfaces (GUIs) for tasks such as bit gray slicing, filtering, histogram equalization, and more. Designed for both educational and practical applications, it aims to enhance understanding of image processing concepts.

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