Howtosignalprocessing
- What is compressive sensing theory?
- What is compressed sensing in image processing?
- What is compressed sensing in machine learning?
- Why is compressed sensing important?
What is compressive sensing theory?
The compressive sensing theory states that the signal can be reconstructed using just a small set of randomly acquired samples if it has a sparse (concise) representation in certain transform domain.
What is compressed sensing in image processing?
Compressed sensing (CS) is an image acquisition method, where only few random measurements are taken instead of taking all the necessary samples as suggested by Nyquist sampling theorem. It is one of the most active research areas in the past decade.
What is compressed sensing in machine learning?
Compressed sensing (CS) is a technique in signal processing which reconstructs any given signal at a rate less than that of Nyquist's' rate given that the signal is sparse and incoherent in nature. The main focus of CS is to find a random matrix which reconstructs the original signal using as few samples as possible.
Why is compressed sensing important?
Compressive sensing possesses several advantages, such as the much smaller need for sensory devices, much less memory storage, higher data transmission rate, many times less power consumption. Due to all these advantages, compressive sensing has been used in a wide range of applications.
