Howtosignalprocessing
- What is required for compressed sensing?
- What are the advantages of compressed sensing?
- What is compressed sensing in image processing?
- What is compressive sensing theory?
What is required for compressed sensing?
The compressed sensing framework mainly includes three parts: sparse representation, compressed observation, and reconstruction model and method. Among them, the sparsity of signal and sparse representation are the basic requirement for compressed sensing.
What are the advantages of compressed sensing?
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.
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 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.
