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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In image fusion tasks, images obtained from different sources exhibitdistinct properties. Consequently, treating them uniformly with a single-branchnetwork can lead to inadequate feature extraction. Additionally, numerous workshave demonstrated that multi-scaled networks capture information moresufficiently than single-scaled models in pixel-level computer vision problems.Considering these factors, we propose U2Net, a spatial-spectral-integrateddouble U-shape network for image fusion. The U2Net utilizes a spatial U-Net anda spectral U-Net to extract spatial details and spectral characteristics, whichallows for the discriminative and hierarchical learning of features fromdiverse images. In contrast to most previous works that merely employconcatenation to merge spatial and spectral information, this paper introducesa novel spatial-spectral integration structure called S2Block, which combinesfeature maps from different sources in a logical and effective way. We conducta series of experiments on two image fusion tasks, including remote sensingpansharpening and hyperspectral image super-resolution (HISR). The U2Netoutperforms representative state-of-the-art (SOTA) approaches in bothquantitative and qualitative evaluations, demonstrating the superiority of ourmethod. The code is available at https://github.com/PSRben/U2Net.

U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion