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Solving Large-Scale Inverse Problems via Approximate Message Passing and Optimization.

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Figure

Figure 2.1 For Λ of size 3 × 3, the denoiser ηt : RΛ → R may only process the pixels in gray (thecenter and the four adjacent pixels).
Figure 2.2 Illustration of the definition of “missing” entries in a sliding window inFive entries, which are in light gray, are missing, hence we define their value to be the average of theexisting ones, ¯coordinate (1 Z2
Figure 2.3 Numerical example. From left to right: ground-truth image generated by the MRF de-scribed in Section 2.2.3.1, image reconstructed by AMP with a separable Bayesian denoiser (computedfrom the incorrect assumption that the signal is generated from
Figure 2.4 Numerical verification that the empirical MSE achieved by AMP with sliding-window de-noisers is tracked by state evolution
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