Figure 1

Diagram for update operations in the learned fixed point iteration (L-FPI) to solve (L-CFP). Here \(R_{\Theta }\) is comprised of a finite sequence of applications of (possibly) distinct affine mappings (e.g. convolutions) and nonlinearities (e.g. projections on the nonnegative orthant i.e. ReLUs). For each \(k\in {\mathbb {N}}\), we let \({\mathcal {A}}_{d}^{k}\) be a projection-based algorithmic operator. The parameters Θ of \(R_{\Theta }\) are tuned in an offline process by solving (9) to ensure signals are faithfully recovered.