English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep image prior (DIP) serves as a good inductive bias for diverse inverseproblems. Among them, denoising is known to be particularly challenging for theDIP due to noise fitting with the requirement of an early stopping. To addressthe issue, we first analyze the DIP by the notion of effective degrees offreedom (DF) to monitor the optimization progress and propose a principledstopping criterion before fitting to noise without access of a paired groundtruth image for Gaussian noise. We also propose the `stochastic temporalensemble (STE)' method for incorporating techniques to further improve DIP'sperformance for denoising. We additionally extend our method to Poisson noise.Our empirical validations show that given a single noisy image, our methoddenoises the image while preserving rich textual details. Further, our approachoutperforms prior arts in LPIPS by large margins with comparable PSNR and SSIMon seven different datasets.

Rethinking Deep Image Prior for Denoising