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/

Zendy Plus

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

Zendy Tools

Zendy Open

Discriminative learning-based image denoisers have achieved promisingperformance on synthetic noises such as Additive White Gaussian Noise (AWGN).The synthetic noises adopted in most previous work are pixel-independent, butreal noises are mostly spatially/channel-correlated andspatially/channel-variant. This domain gap yields unsatisfied performance onimages with real noises if the model is only trained with AWGN. In this paper,we propose a novel approach to boost the performance of a real image denoiserwhich is trained only with synthetic pixel-independent noise data dominated byAWGN. First, we train a deep model that consists of a noise estimator and adenoiser with mixed AWGN and Random Value Impulse Noise (RVIN). We theninvestigate Pixel-shuffle Down-sampling (PD) strategy to adapt the trainedmodel to real noises. Extensive experiments demonstrate the effectiveness andgeneralization of the proposed approach. Notably, our method achievesstate-of-the-art performance on real sRGB images in the DND benchmark amongmodels trained with synthetic noises. Codes are available athttps://github.com/yzhouas/PD-Denoising-pytorch.

When AWGN-based Denoiser Meets Real Noises