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

RGB-NIR fusion is a promising method for low-light imaging. However,high-intensity noise in low-light images amplifies the effect of structureinconsistency between RGB-NIR images, which fails existing algorithms. Tohandle this, we propose a new RGB-NIR fusion algorithm called Dark Vision Net(DVN) with two technical novelties: Deep Structure and Deep Inconsistency Prior(DIP). The Deep Structure extracts clear structure details in deep multiscalefeature space rather than raw input space, which is more robust to noisyinputs. Based on the deep structures from both RGB and NIR domains, weintroduce the DIP to leverage the structure inconsistency to guide the fusionof RGB-NIR. Benefiting from this, the proposed DVN obtains high-qualitylowlight images without the visual artifacts. We also propose a new datasetcalled Dark Vision Dataset (DVD), consisting of aligned RGB-NIR image pairs, asthe first public RGBNIR fusion benchmark. Quantitative and qualitative resultson the proposed benchmark show that DVN significantly outperforms othercomparison algorithms in PSNR and SSIM, especially in extremely low lightconditions.

DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior