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

Supervised networks address the task of low-light enhancement using pairedimages. However, collecting a wide variety of low-light/clean paired images istedious as the scene needs to remain static during imaging. In this paper, wepropose an unsupervised low-light enhancement network using contextguidedillumination-adaptive norm (CIN). Inspired by coarse to fine methods, wepropose to address this task in two stages. In stage-I, a pixel amplifiermodule (PAM) is used to generate a coarse estimate with an overall improvementin visibility and aesthetic quality. Stage-II further enhances the saturateddark pixels and scene properties of the image using CIN. Different ablationstudies show the importance of PAM and CIN in improving the visible quality ofthe image. Next, we propose a region-adaptive single input multiple output(SIMO) model that can generate multiple enhanced images from a single lowlightimage. The objective of SIMO is to let users choose the image of their likingfrom a pool of enhanced images. Human subjective analysis of SIMO results showsthat the distribution of preferred images varies, endorsing the importance ofSIMO-type models. Lastly, we propose a low-light road scene (LLRS) datasethaving an unpaired collection of low-light and clean scenes. Unlike existingdatasets, the clean and low-light scenes in LLRS are real and captured usingfixed camera settings. Exhaustive comparisons on publicly available datasets,and the proposed dataset reveal that the results of our model outperform priorart quantitatively and qualitatively.

Unsupervised network for low-light enhancement