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This was achieved in part by means of optimizing the projection angles that were processed, thus favoring the use of more angles in the lateral (i.e., horizontal) direction and fewer angles in the AP direction.
The following are the steps of the proposed algorithm for optimizing the projection matrix, supposing the sparsifying matrix is known.
Afterwards, a test image was used to assess the recovery algorithm performance in CS before and after optimizing the projection matrix.
It also demonstrated that after optimizing the projection matrix using the proposed method, the CS performance was greatly improved than using random projection matrix and the already existed optimization methods.
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With the help of artificial fish-swarm algorithm (AFSA), it optimizes the projection index function, seeks for the optimal projection direction, and improves AFSA with the application of self-adaptive artificial fish step and crowding factor.
Algorithm is based on a flexible and scalable Cellular Neural Networks (CNN) architecture specifically designed to optimize the projection of CNN kernels on a programmable circuit.
Step 3: Optimize the projection objective function.
Step 4. Optimize the projection matrix: use the three methods mentioned above to optimize the projection matrix.
By solving the problem of (15), they achieved to optimize the projection matrix.
In order to maximize (R_{mathrm{F}}), it is necessary to optimize the projection angle ϕ.
In this paper, a method to optimize the projection matrix is proposed from the perspective of ETF design [9].
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