Another question is the robustness of the sparsity to the frame desynchronization between the transmitting and the receiving part of the communication chain.
One approach for video reconstruction exploits the sparsity of inter-frame differences [7].
By iterative numerical computing, we solve this sparseland-prior reconstruction model, and acquire a sparsity-preferred interpolated frame.
We utilize the sparsity in TIHP tight frame instead of shearlet sparsity for reconstruction from the far-field data.
For the latter, the data is distributed in adjacent data segments [41 43], which is not necessary at the situation of block sparsity based on tensor frame.
In Fig. 5 a, the black curve displays the Gini index for the joint spectro-spatial sparsity, computed in every frame on a vector with K·R spectral coefficients.
The fundamental premise in CS is that certain classes of signals, such as natural images, have a succinct representation in terms of a sparsity inducing basis, or frame, such that only a few coefficients are significant and the remaining coefficients are negligibly small.
The Gini index for spatial sparsity in each subband and frame is computed on a vector of R spectral coefficients, as exemplarily shown using the black boxes for the first subband in frame 400.
The Max-K algorithm can be regarded as a parameterized relaxation with respect to RAAR [8] of utilizing K sparse constraint; therefore, we chose the RAAR method with sparsity constraint in the TIHP tight frame for comparison.
where z is the image reconstructed from a sparse x-let coefficient vector, v is a positive constant depending on the statistical parameters of the signal model and on the noise variance (see Equation 14 below), and Φ* is the x-let frame used to impose sparsity.
