Single underwater image enhancement based on the reconstruction from gradients

Li, Wujing; Yang, Ximing J.; Liu, Yuze; Ou, Xianfeng

doi:10.1007/s11042-022-14158-3

Cited by 1 publication

(1 citation statement)

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“…With the increasing research on the ocean, the complexity and variability of the marine environment make it challenging to explore the ocean. Light produces color-bias degradation during transmission and reconstruction of images to achieve image enhancement (Li et al, 2022b). This study investigates the effect of turbulence on the performance of marine object recognition, where feature extraction and recognition of objects become challenging in turbulent environments.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%