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

The vast majority of standard image and video content available online isrepresented in display-encoded color spaces, in which pixel values areconveniently scaled to a limited range (0-1) and the color distribution isapproximately perceptually uniform. In contrast, both camera RAW and highdynamic range (HDR) images are often represented in linear color spaces, inwhich color values are linearly related to colorimetric quantities of light.While training on commonly available display-encoded images is awell-established practice, there is no consensus on how neural networks shouldbe trained for tasks on RAW and HDR images in linear color spaces. In thiswork, we test several approaches on three popular image restorationapplications: denoising, deblurring, and single-image super-resolution. Weexamine whether HDR/RAW images need to be display-encoded using populartransfer functions (PQ, PU21, mu-law), or whether it is better to train inlinear color spaces, but use loss functions that correct for perceptualnon-uniformity. Our results indicate that neural networks train significantlybetter on HDR and RAW images represented in display-encoded color spaces, whichoffer better perceptual uniformity than linear spaces. This small change to thetraining strategy can bring a very substantial gain in performance, up to 10-15dB.

Training Neural Networks on RAW and HDR Images for Restoration Tasks