An unrolled graph denoiser combining a truncated Taylor expansion with fixed-step conjugate gradient is proven to realize only polynomial graph filters of degree at most K(m-1), a measure-zero subset of its nominal degree budget.
Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,
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Unrolling a Graph-Laplacian Denoiser Realizes Only Compositions of Polynomial Graph Filters
An unrolled graph denoiser combining a truncated Taylor expansion with fixed-step conjugate gradient is proven to realize only polynomial graph filters of degree at most K(m-1), a measure-zero subset of its nominal degree budget.