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

Image-to-Image translation (I2I) is a subtype of Machine Learning (ML) thathas tremendous potential in applications where two domains of images and theneed for translation between the two exist, such as the removal of fog. Forexample, this could be useful for autonomous vehicles, which currently strugglewith adverse weather conditions like fog. However, datasets for I2I tasks arenot abundant and typically hard to acquire. Here, we introduce STEREOFOG, adataset comprised of $10,067$ paired fogged and clear images, captured using acustom-built device, with the purpose of exploring I2I's potential in thisdomain. It is the only real-world dataset of this kind to the best of ourknowledge. Furthermore, we apply and optimize the pix2pix I2I ML framework tothis dataset. With the final model achieving an average ComplexWavelet-Structural Similarity (CW-SSIM) score of $0.76$, we prove thetechnique's suitability for the problem.

STEREOFOG -- Computational DeFogging via Image-to-Image Translation on a real-world Dataset