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基于色彩校正和互补双图像多尺度融合的水下图像增强

Underwater image enhancement based on color correction and complementary dual image multi-scale fusion.

作者信息

Lei Xiaoyan, Wang Huibin, Shen Jie, Liu Haiyun

出版信息

Appl Opt. 2022 Jun 10;61(17):5304-5314. doi: 10.1364/AO.456368.

Abstract

Underwater images often suffer from color cast, poor contrast, and detail loss owing to the scattering and absorption of light in water. To solve these problems, we propose what we believe to be a novel underwater image enhancement method based on color correction and dual image multi-scale fusion. We first use the color correction method to solve the problem of color cast, and we compensate the other two-color channels with the highest mean value color channel; further, all the color channels are dynamically stretched. Next, a complementary dual image multi-scale fusion method is used to improve the contrast, pairs of complementary adaptive gamma correction with weighted distribution enhanced images are used as the two inputs of multi-scale fusion, and appropriate weight maps are selected. Then, a multi-scale detail-sharpening method is used to enhance the image details. Qualitative and quantitative evaluations prove that the proposed method can produce high-quality underwater images. Moreover, the proposed method has relatively high evaluator values compared to the state-of-the-art methods.

摘要

水下图像常常由于光在水中的散射和吸收而出现偏色、对比度差以及细节丢失等问题。为了解决这些问题,我们提出了一种我们认为新颖的基于色彩校正和双图像多尺度融合的水下图像增强方法。我们首先使用色彩校正方法来解决偏色问题,并用均值最高的颜色通道补偿其他两个颜色通道;此外,对所有颜色通道进行动态拉伸。接下来,使用一种互补双图像多尺度融合方法来提高对比度,将具有加权分布增强的互补自适应伽马校正图像对用作多尺度融合的两个输入,并选择合适的权重图。然后,使用多尺度细节锐化方法来增强图像细节。定性和定量评估证明,所提出的方法能够生成高质量的水下图像。此外,与现有最先进方法相比,所提出的方法具有相对较高的评估值。

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