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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Nona-Bayer colour filter array (CFA) pattern is considered one of the mostviable alternatives to traditional Bayer patterns. Despite the substantialadvantages, such non-Bayer CFA patterns are susceptible to produce visualartefacts while reconstructing RGB images from noisy sensor data. This studyaddresses the challenges of learning RGB image reconstruction from noisyNona-Bayer CFA comprehensively. We propose a novel spatial-asymmetric attentionmodule to jointly learn bi-direction transformation and large-kernel globalattention to reduce the visual artefacts. We combine our proposed module withadversarial learning to produce plausible images from Nona-Bayer CFA. Thefeasibility of the proposed method has been verified and compared with thestate-of-the-art image reconstruction method. The experiments reveal that theproposed method can reconstruct RGB images from noisy Nona-Bayer CFA withoutproducing any visually disturbing artefacts. Also, it can outperform thestate-of-the-art image reconstruction method in both qualitative andquantitative comparison. Code available:https://github.com/sharif-apu/SAGAN_BMVC21.

SAGAN: Adversarial Spatial-asymmetric Attention for Noisy Nona-Bayer Reconstruction