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Learning Graph Regularisation for Guided Super-Resolution
[article]
2022
arXiv
pre-print
We introduce a novel formulation for guided super-resolution. Its core is a differentiable optimisation layer that operates on a learned affinity graph. The learned graph potentials make it possible to leverage rich contextual information from the guide image, while the explicit graph optimisation within the architecture guarantees rigorous fidelity of the high-resolution target to the low-resolution source. With the decision to employ the source as a constraint rather than only as an input to
arXiv:2203.14297v1
fatcat:jrvlo7vmyncfbdbntkxdo3j7v4