Underwater Color Disparities: Cues for Enhancing Underwater Images Toward Natural Color Consistencies

Wang, Hao; Sun, Shixin; Ren, Peng

doi:10.1109/tcsvt.2023.3289566

Cited by 14 publications

(1 citation statement)

References 57 publications

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“…The method allows deep learning convolutional autoencoders to utilize multiple metadata sourced to normalize their learning. Wang et al (2023d) proposed an adaptive attenuation channel compensation method for optimal channel precorrection and a guided fusion method for eliminating color deviation in RGB color space. Miao et al (2021) proposed a method for hull modeling and identification of operational objectives based on 3D point clouds collected by a laser measurement system mounted on a ship loader.…”

Section: Image Object Recognitionmentioning

confidence: 99%

Multi-scale aware turbulence network for underwater object recognition

Zhou,

Cai,

Jia

et al. 2024

Front. Mar. Sci.

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Underwater imagery is subject to distortion, and the presence of turbulence in the fluid medium poses difficulties in accurately discerning objects. To tackle these challenges pertaining to feature extraction, this research paper presents a novel approach called the multi-scale aware turbulence network (MATNet) method for underwater object identification. More specifically, the paper introduces a module known as the multi-scale feature extraction pyramid network module, which incorporates dense linking strategies and position learning strategies to preprocess object contour features and texture features. This module facilitates the efficient extraction of multi-scale features, thereby enhancing the effectiveness of the identification process. Following that, the extracted features undergo refinement through comparison with positive and negative samples. Ultimately, the study introduces multi-scale object recognition techniques and establishes a multi-scale object recognition network for the precise identification of underwater objects, utilizing the enhanced multi-scale features. This process entails rectifying the distorted image and subsequently recognizing the rectified object. Extensive experiments conducted on an underwater distorted image enhancement dataset demonstrate that the proposed method surpasses state-of-the-art approaches in both qualitative and quantitative evaluations.

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Section: Image Object Recognitionmentioning

confidence: 99%