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

Hazy images are often subject to color distortion, blurring, and othervisible quality degradation. Some existing CNN-based methods have greatperformance on removing homogeneous haze, but they are not robust innon-homogeneous case. The reasons are mainly in two folds. Firstly, due to thecomplicated haze distribution, texture details are easy to be lost during thedehazing process. Secondly, since the training pairs are hard to be collected,training on limited data can easily lead to over-fitting problem. To tacklethese two issues, we introduce a novel dehazing network using 2D discretewavelet transform, namely DW-GAN. Specifically, we propose a two-branch networkto deal with the aforementioned problems. By utilizing wavelet transform in DWTbranch, our proposed method can retain more high-frequency knowledge in featuremaps. In order to prevent over-fitting, ImageNet pre-trained Res2Net is adoptedin the knowledge adaptation branch. Owing to the robust feature representationsof ImageNet pre-training, the generalization ability of our network is improveddramatically. Finally, a patch-based discriminator is used to reduce artifactsof the restored images. Extensive experimental results demonstrate that theproposed method outperforms the state-of-the-arts quantitatively andqualitatively.

DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing