Constant used to determine the minimum samples in Compressed Sensing

1. What is compressed sensing used for?
2. What is compressive sensing theory?
3. What is compressed sensing in image processing?
4. What is sparse signal?

What is compressed sensing used for?

Compressed sensing can be used to improve image reconstruction in holography by increasing the number of voxels one can infer from a single hologram. It is also used for image retrieval from undersampled measurements in optical and millimeter-wave holography.

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 sparse signal?

Sparse signals are characterized by a few nonzero coefficients in one of their transformation domains. This was the main premise in designing signal compression algorithms. Compressive sensing as a new approach employs the sparsity property as a precondition for signal recovery.