Digital-Image-Processing-Assignments

This repository contains a collection of image processing assignments completed during my Master’s in Computer Science. The projects explore a variety of foundational and advanced topics such as filtering, edge detection, histogram equalization, histogram specification, and frequency domain analysis. Implementations are done primarily in Python, and demonstrate both theoretical understanding and practical application of image processing techniques.

Below are the Assignments:

1. Consider a gray image Plot the histogram the image Identify the mode from the histogram Select this mode as the threshold Binarize the original image with respect to the mode
2. Consider a gray image Write a program to find corresponding mean filter image(for first order and second order both) Write a program to find corresponding median filter image(for first order and second order both) For both above questions there should be provision of customization of order.
3. Like assignment 2, make a mode filtering , and what impact you get from that image.
4. When we increase the order of masking what is influence of the neighbourhood in mode, mean, median filtering
5. Make 3x3 mask for mode and median filtering
6. Write a program to produce differential - a. Row wise, Column wise Write a program for producing b. Δxx , Δxy , Δyy Hence for a. And b. Program produce I ) Laplasian without cross effect II ) Laplasian with cross effect
7. i) Program for transforming [a,b] [c,d] → ii) Frame a program to design transfer for image negation
8. A. Given a digital image i) Construct Histogram ii) Deduce the related histogram (Normalized) ii) Determine the corresponding probability density iv) Find out the equalized histogram v) Find out the similarity between the original image and histogram equalized output image B. Same for Histogram Specification.
9. Write a program to generate an arbitrarily rectangular shaped mask (2a+1 x 2b+1,a,b may equal or may not be) where a and b may be fixed in customised way. Hence apply this mask on arbitrary gray images .
10. Write down a program to derive 2D fourier transformation using separability properties of kernel(Forward as well as Inverse). Hence verify the frequency image remains invariant using row major and column major transformation.
11. Translation a. Frequency Spatial → b. Spatial Frequency → Where x0 and y0 are user input.
12. Verify that the degree of rotation in spatial domain and frequency domain are equal in Fourier Transformation. Write a program where an arbitrary image is taken as an input and check it.
13. Apply DFT and FFT on the same image. Show the difference on the time of execution. Vary the image size from small to large image.
14. In image registration , calculate the Cfg value, then calculate g2 value. Then normalized value = ( Cfg / g2 ).
15. Conversion : a. RGB HSI → b. HSI RGB →