2018
DOI: 10.3390/robotics7010014
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An Underwater Image Enhancement Algorithm for Environment Recognition and Robot Navigation

Abstract: There are many tasks that require clear and easily recognizable images in the field of underwater robotics and marine science, such as underwater target detection and identification of robot navigation and obstacle avoidance. However, water turbidity makes the underwater image quality too low to recognize. This paper proposes the use of the dark channel prior model for underwater environment recognition, in which underwater reflection models are used to obtain enhanced images. The proposed approach achieves ve… Show more

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Cited by 21 publications
(5 citation statements)
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“…(1) The Multiscale Fusion Module (MFM) enhances effective connections of spatial information among the three branches. (2) The RDB utilize local dense connections to better leverage all layers and adaptively retain accumulated features through LFF. (3) The Three-Group Structure (3GS) increases the depth and expressive power of the network.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…(1) The Multiscale Fusion Module (MFM) enhances effective connections of spatial information among the three branches. (2) The RDB utilize local dense connections to better leverage all layers and adaptively retain accumulated features through LFF. (3) The Three-Group Structure (3GS) increases the depth and expressive power of the network.…”
Section: Methodsmentioning
confidence: 99%
“…With the rapid advancement of computer technology, the widespread application of computer vision systems in underwater image enhancement has received increasing attention. Underwater images serve as essential information sources in marine environments, playing critical roles in marine resource exploration [1], underwater robot navigation [2], underwater monitoring [3], and other fields. However, the optical characteristics and water quality conditions in underwater environments often lead to challenges such as light attenuation, scattering, and absorption, resulting in low image quality, including blurriness, dimness, and lack of details.…”
Section: Introductionmentioning
confidence: 99%
“…Hafiz Tayyab Rauf et al [24] developed a DL scheme centered on the CNN model for fish species recognition. To improve classification performance, VGGNet architecture was subjected to deep supervision by incorporating four CLs for the network's every level of training.…”
Section: Similar Research Work For Comparisonmentioning
confidence: 99%
“…Also we know that major percentage of population of Kerala depends on the marine life for their livelihood. Moreover, statistics shows that out of 80,500 species of living vertebrates nearly half is fish species [25]. Besides this it is also known that 70.9% of earth's surface which constitutes 97% of earth water, thus from the above facts we can assume that how important is to protect and safeguard marine life.…”
Section: Introductionmentioning
confidence: 99%