digital image processing assignment solution

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Digital Image Processing Assignment solutions

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Following questions were part of the assignment for the digital image processing course.

Histogram.py was written to find the histogram for the grayscale images.

Frequency lowpass.py was written to check the frequency domain ideal low pass filter properties

Filter the image characters.tif in the frequency domain using the Gaussian low pass filter given by 𝐻 (𝑢, 𝑣) =exp (−𝐷2(𝑢, 𝑣)/2𝐷02), where all the terms are as explained in part b. For 𝐷0=100, compare the result with that of the ILPF. This was done using Gauss-lowpass.py

Use homomorphic filtering to enhance the contrast of the image PET_image.tif. Use the following filter to perform the high pass filtering 𝐷 (𝑢, 𝑣) =(𝛾𝐻−𝛾𝐿) [1−exp (−𝐷2(𝑢, 𝑣)/2𝐷02)] + 𝛾𝐿, where 𝛾𝐻, 𝛾𝐿 and 𝐷0 are the parameters that you need to adjust through experimentation. This was done using Homomorphic filtering.py

5.a. Mitigate the noise in the image noisy.tif by filtering it with a square averaging mask of sizes 5,10 and 15. What do you notice with increasing mask size.

b. Use high boost filtering to sharpen the denoised image from part a. Choose the scaling constant for the high pass component that minimizes the mean squared error between the sharpened image and the image characters.tif. This was done using Spatial domain filtering.py

Generate a 𝑀×𝑁 sinusoidal image sin(2𝜋𝑢0𝑚/𝑀+2𝜋𝑣0𝑛/𝑁) for 𝑀=𝑁=1001, 𝑢0=100 and 𝑣0=200 and compute its DFT. To visualize the DFT of an image take logarithm of the magnitude spectrum. This was done using DFT visualizer.py

Image Deblurring: Deblur the images Blurred-LowNoise.png (Noise Standard Deviation (𝜎)=1), Blurred-MedNoise.png (𝜎=5) and Blurred-HighNoise.png (𝜎=10) which have been blurred by the kernel BlurKernel.mat using inverse filtering, constrained least squares filtering, wiener filtering

Python 100.0%

INF2310 - Digital Image Processing

University of Utah

School of computing, digital image processing.

Fall Semester 2018

WEB 2230 MW 1:25-2:45

Instructor: Thomas C. Henderson

Overview of Course

Course objectives, prerequisites, course description, software used to support class.

Required Materials
Projects and Assignments

Class Schedule and Assignments

Grading information, assignment due time, assignment late policy, individual work, registration.

American Disabilities Act
Link to Course Info and Docs
Link to Programming Tips
Link to Code
Link to Lectures
Lab Report Format
Link to Testing and Data
Link to Problems and handin summary
Link to Solutions
College Guidelines

Survey Digital Image Processing basics :

Image Representation Basics
Enhancement
Morphological Operations
Convolutional Networks
Deep Learning
Segmentation: Features
Object Representation and Description
Object Recognition

Prerequisites: Full major status in Computer Science or Computer Engineering.

We will work on the problems and solutions of digital image processing.

Students must develop codes in Matlab.

Suggested Materials

We will use:

Digital Image Processing, Gonzalez and Woods, 4th Edition, (not required, but on reserve)

Assignments

There are 2 major types of assignments:

Problem Assignments: Matlab functions must be developed and delivered through handin.
Quizzes: Weekly (short) quizzes will be held during the semester.

Class Syllabus

The lectures will cover the text on the following schedule (may vary some during semester to accommodate progress):

The lectures and assignments will cover the text as we progress through the semester. Assignments will usually be handed out on Monday and due on a Wednesday after the material is covered.

Instructor

Instructor:

Thomas C. Henderson , Professor

[email protected]

801-581-3601

801-585-3743

Office Hours (2871 WEB): By appointment.

Office Hours :

The grading distribution will be as follows:

Problems: 70%
In-Class Tests and Participation: 30%

You are expected to make a good effort on all assignments and in-class discussion based on a careful reading of the assigned material. I will assign a grade based on how reasonable your solution is given the difficulty of the assignment, the time required, and the style and content of the solution. My goal is to look at all your work, and to assign a grade based on your participation, effort and results. It's better to ask questions before and during an assignment, than to try and understand what went wrong after it's due. The proportions given above delineate how I intend to apportion the weight of the various work in the course.

Unless otherwise stated in an assignment, all assignments will be due by classtime on the assignment due date. You should handin all functions developed for the assignment. The time that we use for an assignment is the submit time.

Policy and Appeals Procedure

See Academic misconduct page as well as the Code of Student Rights and Responsibilities, SoC policies page, or the Class Schedule for more details.

Appeals of Grades and other Academic Actions

If a student believes that an academic action is arbitrary or capricious he/she should discuss the action with the involved faculty member and attempt to resolve. If unable to resolve, the student may appeal the action in accordance with the following procedure:

Appeal to Department Chair who should be notified in writing within 40 working days; chair must notify student of a decision with 15 days. If faculty member or student disagrees with decision, then,
Appeal to Academic Appeals Committee (see flyers posted in MEB and EMCB for members of committee). See II Section D, Code of Student Rights and Responsibilities for details on Academic Appeals Committee hearings.

No late work is accepted.

The purpose of the assignments is to improve your skills at solving problems and demonstrating that you understand the class material. Collaboration with other class members is acceptable in understanding problems or software tools. For any individual assignments or work turned in, you must do your own work. Using someone else's work or giving someone else your work is considered plagiarism and will be dealt with using standard College and University procedures (i.e., failure of assignment and class). The SoC policy states: "As defined in the University Code of Student Rights and Responsibilities, academic misconduct includes, but is not limited to, cheating, misrepresenting one's work, inappropriately collaborating, plagiarism, and fabrication or falsification of information. It also includes facilitating academic misconduct by intentionally helping or attempting to help another student to commit an act of academic misconduct. A primary example of academic misconduct would be submitting as one's own, work that is copied from an outside source." (See cheating_policy.pdf and SoC_ack_form.pdf in Link to Class Info and Docs; also see university web page for the full academic calendar Academic misconduct page ).

See university web page for the full academic calendar ( Calendar web page ). See the university web page for a copy of the withdraw guidelines as well, or see the Student Code .

American with Disabilities Act ( ADA )

The University conforms to all standards of the ADA . If you wish to qualify for exemptions under this act, notify the Center for Disabled Students Services, 160 Union . The University of Utah seeks to provide equal access to its programs, services and activities for people with disabilities. If you will need accommodations in the class, reasonable prior notice needs to be given to the Center for Disability Services, 162 Olpin Union Building, 581-5020 (V/TDD). CDS will work with you and the instructor to make arrangements for accommodations. All written information in this course can be made available in alternative format with prior notification to the Center for Disability Services .

Department of Computer Science and Engineering

Cs474/674 image processing and interpretation (fall 2023).

M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision , Cengage Learning, 2015.
S. Birchfield, Image Processing and Analysis , Cengage Learning, 2018.

Prerequisites

Course outline (tentative), exams and assignments, course policies, useful information.

Math Review Material (from textbook)

Sample Exams

Course Overview
PGM Image File Format
Introduction to Image Processing
Math Review
Fourier Transform (see also Fourier Transform Pairs 1 and Fourier Transform Pairs 2)
Midterm Review
Fast Fourier Transform (FFT)
Convolution
Sampling and Aliasing
Image Restoration
Image Compression (also, see Image Compression Techniques and Survey )
Final Review
Short Time Fourier Transform (STFT) (study chapters 1 and 2 from Wavelet Tutorial )
No time to cover this but here is the link if you are interested in learning more about wavelets: Wavelets (also, see notes part 1 , and part 2 )
Multiresolution Analysis

Homework Assignments

Homework 7 (Wavelets - except problems 7.13 and 7.14) (7.9, p 521 >> 6.44, p 525) (7.12, 7.13, p 522 >> 6.36, 6.37, p 524) (7.14, p 522 >> 6.38, p 524) (7.16, p 522 >> 6.40, p 524) (7.19, p 523 >> 6.41, p 525) (7.21, p 523 >> 6.43, p 525) (7.24, p 523 >> 6.45, p 525) Solutions

Programming Assignments

Pogramming Assignment 3 (Due date (extended): 11/20/2023) powerpoint file fft.c , documentation , Rect_128.txt
Project 6 (Due date: 12/14/98) (download data) ( penny_head.gif and penny_tails.gif ) ( nickel_head.gif and nickel_tails.gif ) ( dime_head.gif and dime_tails.gif ) ( quarter_head.gif and quarter_tails.gif ) coins1.gif $0.36 coins2.gif $0.36 (same as coins1 - different lighting) coins3.gif $0.51 coins4.gif $0.51 (same as coins3 - different scale) coins5.gif $0.66 coins6.gif $0.65 (occlusion) coins8.gif $0.47 coins9.gif $0.50 coins10.gif $0.11 coins11.gif $0.11 (same as 10 - different scale) coins16.gif $0.36 coins17.gif $0.60 (different viewpoint)

Sample Presentation Topics (Graduate Students Only)

Presentation guidelines.