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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Patch-based approaches such as 3D block matching (BM3D) and non-local Bayes(NLB) are widely accepted filters for removing Gaussian noise from single-frameimages. In this work, we propose three extensions for these filters when thereexist multiple frames of the same scene. The first of them employs referencepatches on every frame instead of a commonly used single reference framemethod, thus utilizing the complete available information. The remaining twotechniques use a separable spatio-temporal filter to reduce interactionsbetween dissimilar regions, hence mitigating artifacts. In order to deal withnon-registered datasets we combine all our extensions with robust optical flowcomputation. Two of our proposed multi-frame filters outperform existingextensions on most occasions by a significant margin while also beingcompetitive with a state-of-the-art neural network-based technique. Moreover,one of these two strategies is the fastest among all due to its separabledesign.

Removing Multi-frame Gaussian Noise by Combining Patch-based Filters with Optical Flow