Underwater Image Restoration Based on Adaptive Color Compensation and Dual Transmission Estimation

Wang, Yingbo; Cao, Jie; Rizvi, Saad; Hao, Qun; Fang, Yami

doi:10.1109/access.2020.3037362

Cited by 6 publications

(1 citation statement)

References 39 publications

(55 reference statements)

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“…In this section, several objective evaluation metrics including information entropy (IE) [53], AG [53], underwater image quality measure (UIQM) [54], and patch-based contrast quality index (PCQI) [55] are employed to evaluate the restoration results achieved by different methods. The metrics IE, AG, and PCQI are utilized to quantify the information richness, clarity of detail textures, and contrast of underwater images, respectively.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Underwater image enhancement via color correction and multi-feature image fusion

Ke,

Zhang,

Zhang

et al. 2024

Meas. Sci. Technol.

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The light attenuation underwater causes the actual underwater images to suffer from color cast, low contrast, and weak illumination. To address these issues, an effective fusion-based method is proposed, which realizes color correction, brightness adjustment, contrast, and detail enhancement of underwater images. Concretely, we first design an adaptive color correction method via dominant color channel judgment and lower color channel compensation. Then, we detect the brightness of each input image and propose a gamma correction function based on the gradient of the cumulative histogram to adjust the brightness of the low-light images. Subsequently, global histogram stretching and adaptive fractional differentiation techniques are employed to process the brightness-adjusted image, and then the global contrast-enhanced version and detail-enhanced version are generated respectively. To integrate the advantages of both versions, a channel fusion method based on the Lab color space is used to fuse the luminance and color of the two versions separately. The experimental results demonstrate the effectiveness of the proposed method in improving the color and illumination of underwater images, as well as enhancing the clarity of images. Moreover, the testing results on multiple datasets validate the excellent stability of this method.

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Section: Quantitative Evaluationmentioning

confidence: 99%