Basic Information
- Course Code: CS 754
- Course Name: Advanced Image Processing
- Course Offered In: 2021-‘22
- Semester Season: Spring
- Instructors: Prof. Ajit Rajwade
- Prerequisites: Some probability course, Signal processing (weak pre-req)
- Difficulty (1 being easy and 5 being tough): 4
Course Content
The courses tackles inverse problems, a.k.a. reconstruction problems in the context of image processing, and broadly covers the topics of compressed sensing, tomography, dictionary learning, low rank matrix processing and statistics of natural images. Most of the content is useful for signal processing in general and requires a good understanding of linear algebra and probability. There is a good balance of theory and applications.
Feedback on Lectures
Lectures were well-paced, easy to understand and made use of slides. Prof taught very well. All of my lectures were pre-recorded, so the prof kept separate meets to discuss the lecture content and resolve doubts. Overall, the lectures were enjoyable.
Feedback on Evaluations
Course evaluation was heavily focussed on assignments and an end-term project, with weightage of 50% and 25% respectively. Both could be done in a group of two. The assignments involved both theory and coding questions, which helped better appreciate the lecture content. The project involved a paper implementation. The mid-sem (10%) and end-sem (15%) were simple and following the lectures was sufficient to score well.
Study Material and Resources
Slides are sufficiently detailed. Most of the content can be further explored through relevant papers.
Final Takeaway
This is a great course to take if you want to explore techniques in signal reconstruction. The content is research-oriented and exciting.