ECE 495: Cameras, Images, and Statistical Inverse Problems
Announcement
Course Information
Lecture: MWF 10:30am - 11:20am
Room: MSEE B010 (in-person)
Lectures will be recorded via BoilerCast, and will be made available in Brightspace (subject to delays).
Instructor: Professor Stanley H. Chan
Room: MSEE 338
Email: stanleychan AT purdue DOT edu
Office Hour: After class, and by appointment
Teaching Assistants:
Nick Chimitt, nchimitt AT purdue DOT edu
Guanzhe Hong, hong288 AT purdue DOT edu
Office Hour: By appointment
Syllabus
Note
Part 1: Understanding Your Camera
Lecture Note 1-1: Cameras in the 21st century (PDF, 6MB)
Lecture Note 1-2: Digital image sensors (PDF, 6MB)
Lecture Note 1-3: Noise
Lecture Note 1-4: Signal-to-Noise Ratio (PDF, 580KB)
Lecture Note 1-5: Dynamic Range (PDF, 2.3MB)
Part 2: Probability and Statistics
Lecture Note 2-1: Gaussian Random Variables (PDF, 500KB)
Lecture Note 2-2: Central Limit Theorem (PDF, 820KB)
Lecture Note 2-3: High-dimensional Random Variables (PDF, 830KB)
Lecture Note 2-4: Basics of Poisson random variables (PDF, 500KB)
Lecture Note 2-5: Physics of Photon Arrivals (PDF, 540KB)
Lecture Note 2-6: Single-Photon and Low Bit-Depth Statistics (PDF, 900KB)
Part 3: Estimation Techniques
Lecture Note 3-1: Maximum-Likelihood Estimation (PDF, 1.6MB)
Lecture Note 3-2: Properties of ML Estimation (PDF, 480KB)
Lecture Note 3-3: Maximum-A-Posteriori Estimation (PDF, 588KB)
Lecture Note 3-4: Minimum Mean-Square Estimation (PDF, 432KB)
Part 4: Denoising
Lecture Note 4-1: Linear Inverse Problems (PDF, 1MB)
Lecture Note 4-2: ADMM Algorithm (PDF, 800KB)
Lecture Note 4-3: Patch Reoccurence
Lecture Note 4-4: Smoothing Filters
Lecture Note 4-5: Variance Stabilizing Transforms
Lecture Note 4-6: Is Denoising Dead?
Part 5: Learning-based Methods
(Not sure if we will have time to get here.)
Lecture Note 5-1: Network Unrolling
Lecture Note 5-2: Knowledge Distillation
Lecture Note 5-3: One-size-fit-all
Lecture Note 5-4: Dynamic Scenes
Homework (30%)
There will be six homework. I will drop the worst one. If there is a
programming problem, you can choose whatever language you like: MATLAB,
Python, Julia, C (I wouldn't recommend it though).
Homework is due 11:59pm Eastern Time on the due day. Please submit your
homework through Gradescope.
URL for Gradescope: HERE
Late homework will not be accepted.
Homework 4
Download (PDF, 240KB)
Data download: See Brightspace
Due March 23, 2022, Wednesday, 11:59pm Eastern Time
Quiz (30%)
There will be six quizes. I will drop the worst one. Each quiz is 30
minutes long. The quizes are conducted right after
the due date of the homework. You will be given a 72 hour window to complete
the 30-min quiz, completely online. During the quiz, I will ask you lecture
questions. I will also ask you homework questions. For example, if in the
homework I ask you to plot a figure, I may ask you to change a parameter and
re-plot the figure. Quizes will be open-book, open-note, open-computer.
However, with only 30 minutes, you probably will not have time to read
anything besides answering the questions. So, please do the homework.
URL for Gradescope: HERE
Quiz 1
Start: Jan 27, 2022 12:01am Eastern Time (Thursday)
End: Jan 30, 2022 11:59pm Eastern Time (Saturday)
Quiz 2
Start: Feb 12, 2022 12:01am Eastern Time (Saturday)
End: Feb 14, 2022 11:59pm Eastern Time (Monday)
Quiz 3
Start: March 3, 2022 12:01am Eastern Time (Thursday)
End: March 5, 2022 11:59pm Eastern Time (Saturday)
Quiz 4
Start: March 24, 2022 12:01am Eastern Time (Thursday)
End: March 26, 2022 11:59pm Eastern Time (Saturday)
Quiz 5
Start: April 9, 2022 12:01am Eastern Time (Saturday)
End: April 11, 2022 11:59pm Eastern Time (Monday)
Quiz 6
Start: April 28, 2022 12:01am Eastern Time (Thursday)
End: April 30, 2022 11:59pm Eastern Time (Saturday)
Seminar Reports (30%)
Attend any THREE of the following seminars. Write a report of no more than 2 pages, 11 points, Times Roman, 1 inch margin. The grading criteria is based on how much thinking you put into each report. Show me that you have thought about these questions.
Mar 1: Yaniv Romano
Date: Mar 1, 2022. 10am Eastern Time
Zoom: Check brightspace
Q1: Summarize the talk in 5-7 sentences
Q2: According to Professor Romano and based on your readings online, why is important to have an accurate of the confidence interval?
Q3: He mentioned that today's models are mostly black boxes, and discussed the concept of a wrapper function. What is it, and why is it relevant?
Q4: In your own words, describe the quantile estimation procedure he presented. What statistical guarantees can his procedure offer?
Q5: He showed an example of the 2020 presidential election. Can you describe how his technique is used?
Q6. What is the most inspiring part of the talk, and what did you learn?
Q7. If you could ask a question to the speaker, what would you ask? Please elaborate your question.
Report is due on March 23, 2022 11:59pm (submit to gradescope)
Mar 22: Joyce Farrell
Date: Mar 22, 2022. 1pm Eastern Time
Zoom: Check brightspace
Q1: Summarize the talk in 5-7 sentences
Q2: Why is physics-based simulation an important tool for the camera industry?
Q3: What are the main tools Dr Farrell used to do the simulation? Can you name a few steps? It might be good to briefly read her paper and summarize the key ideas.
Q4: How do they measure the correctness of the simulator? She mentioned a few key metrics. Can you summarize a few here?
Q5: What are the applications of the simulator, and how is the simulator used in those scenarios? What does the simulator offer?
Q6. What is the most inspiring part of the talk, and what did you learn?
Q7. If you could ask a question to the speaker, what would you ask? Please elaborate your question.
Apr 13: Kyros Kutulakos
Date: Apr 13, 2022. 12pm Eastern Time
Zoom: Check brightspace
Q1: Summarize the talk in 5-7 sentences
Q2: Can describe the basic principle of light transport, and how did the presenter measure lights from different optical paths?
Q3: What are some of the limitations of the technology?
Q4: What can you do with the technology? Mention a few possible applications talked by the speaker.
Q5: He mentioned the concept of dual pixel. What can you use it for?
Q6. What is the most inspiring part of the talk, and what did you learn?
Q7. If you could ask a question to the speaker, what would you ask? Please elaborate your question.
These talks are part of Purdue computational imaging seminar.
Class Participation (10%)
This is a small class and so I will be able to
remember everyone of you, and you will have the chance to interact a lot with
me. The purpose of the class participation is to encourage intellectual
discussions. Come and discuss things with me. There are a few ways I will use to check your participation:
Come to class (I will not take attendence, but with such a small class I will remember you.)
Ask questions
Reply questions in Piazza
Talk to the TAs
Show enthusiasm about learning
You do not need to do everything listed above, nor I will have a systematic
way of checking you. Basically, as long as you do not hide yourself, you will
be fine for this portion of the grade.
COVID and Health Related Issues
So here is the rule of thumb:
Follow Protect Purdue instructions. If you don't know what I am talking about, visit https://protect.purdue.edu/. Follow everything they say.
Additionally:
If you are sick (flu or cold or whatever), please take rest and stay at home. I will record the lectures via BoilerCast.
Please wear a mask in the classroom. While I do not have authority to enforce anything, I recommend you use disposable medical masks instead of cloth masks.
If I am sick, I will either cancel the class or move it to Zoom. Check Brightspace if this happens.
If you ever experience any stress about the course work, please let me or the TAs know. We are here to help you.
Other Class Policy
We will follow these class policies
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