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Image denoising and artefact removal are complex inverse problems admittingmultiple valid solutions. Unsupervised diversity restoration, that is,obtaining a diverse set of possible restorations given a corrupted image, isimportant for ambiguity removal in many applications such as microscopy wherepaired data for supervised training are often unobtainable. In real worldapplications, imaging noise and artefacts are typically hard to model, leadingto unsatisfactory performance of existing unsupervised approaches. This workpresents an interpretable approach for unsupervised and diverse imagerestoration. To this end, we introduce a capable architecture calledHierarchical DivNoising (HDN) based on hierarchical Variational Autoencoder. Weshow that HDN learns an interpretable multi-scale representation of artefactsand we leverage this interpretability to remove imaging artefacts commonlyoccurring in microscopy data. Our method achieves state-of-the-art results ontwelve benchmark image denoising datasets while providing access to a wholedistribution of sensibly restored solutions. Additionally, we demonstrate onthree real microscopy datasets that HDN removes artefacts without supervision,being the first method capable of doing so while generating multiple plausiblerestorations all consistent with the given corrupted image.

Interpretable Unsupervised Diversity Denoising and Artefact Removal