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

Underwater images often have severe quality degradation and distortion due tolight absorption and scattering in the water medium. A hazed image formationmodel is widely used to restore the image quality. It depends on two opticalparameters: the background light and the transmission map. Underwater imagescan also be enhanced by color and contrast correction from the perspective ofimage processing. In this paper, we propose an effective underwater imageenhancement method for underwater images in composition of underwater imagerestoration and color correction. Firstly, a manually annotated backgroundlights (MABLs) database is developed. With reference to the relationshipbetween MABLs and the histogram distributions of various underwater images,robust statistical models of BLs estimation are provided. Next, the TM of Rchannel is roughly estimated based on the new underwater dark channel prior viathe statistic of clear and high resolution underwater images, then a scenedepth map based on the underwater light attenuation prior and an adjustedreversed saturation map are applied to compensate and modify the coarse TM of Rchannel. Next, TMs of G-B channels are estimated based on the difference ofattenuation ratios between R channel and G-B channels. Finally, to improve thecolor and contrast of the restored image with a natural appearance, a variationof white balance is introduced as post-processing. In order to guide thepriority of underwater image enhancement, sufficient evaluations are conductedto discuss the impacts of the key parameters including BL and TM, and theimportance of the color correction. Comparisons with other state-of-the-artmethods demonstrate that our proposed underwater image enhancement method canachieve higher accuracy of estimated BLs, less computation time, more superiorperformance, and more valuable information retention.

Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map