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In next version, we are planning to implement the projection of profiles onto orthonormal bases, which should improve our performances.
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In Algorithms 3.1 and 3.2, there still exists difficulty to implement the projections P C and P Q with respect to the defined norms, especially when C and Q are general closed convex sets.
to implement the plans.
The ability to implement fan beam projection in parallel view in an optical tomography setup is one of the novelties of this research.
In 1999, Solodov and Svaiter [3] proposed an algorithm for solving the VI ( C, f ) in an Euclidean space, known as the double projection algorithm due to the fact that one needs to implement two projections in each iteration.
Two main approaches to implement random projections in a SN are discussed in the literature [163].
In comparison to the previous bright field based cell segmentation techniques presented in the literature, this approach is more straightforward to implement, and the resulting bright field projection image is directly applicable for segmentation using CellProfiler [2] analysis software designed for fluorescent microscopy.
In practice, this step is often related to the (ell_{1}) shrinkage or projection operation being easy to implement, so the cost could be still ignorable in practice.
Therefore, it is necessary to implement adaptive quantization to the projections.
These results suggest that it is advantageous to implement tomographic diagnostic such that the projection image fills the camera chip completely.
To implement these algorithms, one has to compute the projection onto the feasible set C, or onto some related set.
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