Coded aperture design

This paper proposes an adaptive grayscale coded aperture design which combines the advantages of blue noise and block-unblock coding patterns to provide high-quality image reconstructions and redundancy in the sampling.

This paper proposes to design a set of coding patterns such that inter-class and intra-class data structure is preserved after the CSI acquisition in order to improve clustering results directly on the compressed domain. To validate the coding pattern design, an algorithm based on sparse subspace clustering (SSC) is proposed to perform clustering on the compressed measurements.