Abstract:
Compressive sensing requires a sensing matrix that is random enough for good signal reconstruction and has desirable properties like orthogonality or circulancy.
First, generate a full orthogonal circulant matrix, then select a few rows. This paper proposes a refined construction of orthogonal circulant sensing matrices that generates a circulant matrix with only a subset of rows orthogonal.
Thus, the generation method is less constrained, yielding better sensing matrices with the desired properties.
Signal reconstruction compares the partial shift-orthogonal sensing matrix to random and learned ones. This pattern-dependent sensing matrix efficiently detects color patterns and edges from color image measurements.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
