Erratum to: Enhancement of deep-sea floor images obtained by an underwater vehicle and its evaluation by crab recognition

Ahn, Jonghyun; Yasukawa, Shinsuke; Sonoda, Takashi; Ura, Tamaki; Ishii, Kazuo

doi:10.1007/s00773-017-0448-8

Cited by 3 publications

(2 citation statements)

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“…Because of the importance of underwater resources to human beings, the mysteries of the underwater world are being unraveled by humans step by step with the rapid development of underwater robotics. 1 Underwater images are the key to the analysis of underwater information. However, in the underwater environment, due to the absorption and scattering effects of light, uneven illumination, 2 and the influence of an extreme environment, 3 it is often difficult to directly obtain high-quality underwater images.…”

Section: Introductionmentioning

confidence: 99%

“…Our method can effectively eliminate the visual degradation problem caused by hazy content. The main contributions of this paper are summarized as follows: (1) we propose a new underwater image dehazing framework that is divided into three parts and effectively removes the hazy content. (2) First, to give the underwater image a natural look, we use a white balance operation to eliminate the color deviation caused by blue-green light.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale luminance adjustment-guided fusion for the dehazing of underwater images

Xu,

Wang,

Chen

2024

J. Electron. Imag.

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Due to absorption and scattering effects in the underwater medium, acquired underwater images often suffer from hazy content and color casts, which lead to significant degradation of visual quality. In this work, a dehazing model is proposed; it is divided into three parts and effectively eliminates the problem of visual degradation caused by hazy content. The attenuation characteristics of light at different wavelengths are different, which usually leads to significant color bias in the obtained underwater images. Therefore, underwater images are first color corrected, namely by red channel compensation and white balance processing. Next, because the original hazy images are usually underexposed, we manually adjust the luminance of the images. The resulting multi-level luminance images are then fused using the Gaussian-Laplacian pyramid scheme to produce clear underwater images. To verify the validity of our method, we evaluate several representative underwater image datasets and compare our method with several advanced traditional methods and deep learning methods developed in recent years. Our method shows excellent results in both qualitative analysis and quantitative comparison.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%