Analysing Quality of Resilience in Fish4Knowledge Video Analysis Workflows

Nadarajan, Gayathri; Yang, Chorng-Horng; Chen-Burger, Yun-Heh; Tolosana-Calasanz, Rafael; Rana, Omer F.

doi:10.1109/ucc.2013.81

Cited by 1 publication

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References 12 publications

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“…There are several real‐world underwater image data sets, such as Fish4Knowlege, 37 Sea‐thru, 38 RUIE, 39 MABLs 40 . However, existing data sets usually have limited scenes, few degradation characteristics, and insufficient underwater images.…”

Section: Our Methodsmentioning

confidence: 99%

A novel underwater image restoration method based on decomposition network and physical imaging model

Cui

Sun

Jian

et al. 2021

Int J of Intelligent Sys

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Underwater image restoration is one of the significant research in marine engineering and aquatic robotics. However, due to the propagation characteristics of light and the serious turbidity in underwater, the captured images often have chromatic aberration and scattering blur, which brings great challenges to the restoration of the raw image. In this paper, a revised underwater imaging model is proposed first, which reanalyzes the generation of background light from the atmosphere to the underwater and provides important support for underwater color correction. And then a network framework via the revised model is designed, which can decompose the captured image into different components corresponding to the revised model. The proposed network consists of a decomposition architecture with residual blocks that learns a complete separation of clear image and transmittance features. These two features are used along with the raw image to predict the background light. Finally, combining three constraints of the imaging model, the proposed framework can converge rapidly along the desired direction. By comparison with the performance of the state‐of‐the‐art algorithms, the designed network shows excellent visibility and is capable of removing water on both synthetic and real‐world images in different water types.

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