What are some recommended resources for an introduction to compressed sensing?

In summary, the speakers are seeking recommendations for books/articles on compressed sensing that provide a balance between motivation/intuition and formal content. They are open to background material and are particularly interested in applications to neurobiology. Suggestions include a paper by Candes and Watkins and resources by Terry Tao.
  • #1
Physics Monkey
Science Advisor
Homework Helper
1,363
34
Not sure if this is best subforum for this ... but what I'm looking for is some recommendations for books/articles containing a nice introduction to compressed sensing.

I'm not a mathematician although I would like to think I'm relatively mathematically sophisticated. I appreciate proofs in the right places, but what I really want is something with a lot of motivation/intuition along with the formal stuff. But I also don't want something too pedestrian. I have plenty of time so I'm open to background stuff as well.

Any suggestions?
 
Physics news on Phys.org
  • #2
Last edited:

Related to What are some recommended resources for an introduction to compressed sensing?

1. What is compressed sensing?

Compressed sensing is a signal processing technique that allows for the reconstruction of sparse or compressible signals from a small number of linear measurements. It is also known as compressive sensing or sparse sampling.

2. What are some applications of compressed sensing?

Compressed sensing has a wide range of applications, including medical imaging, radar and sonar, wireless communication, and data compression. It has also been applied in fields such as astronomy, geophysics, and machine learning.

3. What is the difference between compressed sensing and traditional signal processing?

Traditional signal processing techniques require a large number of samples to accurately reconstruct a signal, while compressed sensing can achieve the same result with significantly fewer samples. Additionally, compressed sensing allows for the recovery of signals with a high degree of sparsity, meaning that most of the signal coefficients are zero or close to zero.

4. What are some commonly used algorithms for compressed sensing?

Some popular algorithms for compressed sensing include basis pursuit, iterative hard thresholding, and orthogonal matching pursuit. These algorithms use different optimization techniques to reconstruct signals from sparse measurements.

5. What are some recommended references for learning about compressed sensing?

Some recommended references for learning about compressed sensing include the book "Compressed Sensing" by Yonina Eldar and Gitta Kutyniok, and the survey paper "Compressed Sensing" by Emmanuel Candès. There are also many online resources, such as tutorials and lecture notes, available for free.

Similar threads

Other Best resources for self-studying math from K-12?
    • Science and Math Textbooks
Replies
28
Views
3K
Classical Radiometry & Sensing Course: Find Syllabus Here
    • Science and Math Textbooks
Replies
1
Views
752
What are some recommendations for a math and physics topic in high school?
    • STEM Academic Advising
Replies
14
Views
982
Intro Physics Starting fresh, but where to start?
    • Science and Math Textbooks
Replies
5
Views
837
Is LCEVC the Solution for Efficient 8K Television Compression?
    • Programming and Computer Science
Replies
4
Views
701
Intro Math Arithmetic/pre-algebra/basic geometry textbook recommendations
    • Science and Math Textbooks
Replies
14
Views
2K
Book on how to define ideas rigorously
    • Science and Math Textbooks
Replies
6
Views
1K
Is this a good self-study program?
    • STEM Academic Advising
Replies
16
Views
489
B What is the usefulness of formal logic theory?
    • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
Plumbing Compressed air powered water pressure
    • DIY Projects
2
Replies
36
Views
8K

Hot Threads

Recent Insights

Back
Top