PlaSo: Unsupervised Residual Plug and Play for Image Super-Resolution
Résumé
This letter addresses the problem of single image super resolution using Plug-and-play (PnP) frameworks incorporating with residual learning. We propose a novel PnP algorithm, PlaSo, which integrates residual learning mechanisms into diffusion processes to mitigate common challenges such as hallucination artifacts and over-smoothing. Furthermore, by initializing the reverse sampling process from the degraded input rather than random noise, PlaSo achieves faster convergence. Experiments demonstrate that PlaSo consistently outperforms state-of-the-art methods, both qualitatively and quantitatively.
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