Improved Color Balance and Fusion for Enhancement of Underwater Image

Juniie Chen, Yaiuan Wu
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Abstract

In underwater photography, the absorption and scattering of light may probably cause low contrast, blurred images, and color cast. We present an effective method for improving the quality of underwater images that have been degraded by medium scattering and absorption. It combines color compensation with multi-scale image fusion. Color compensation consists of independently correcting the value of $\mathbf{R}$ channel and G-B channel of the input image and adjusting the white balance of the corrected image. After color compensation, the color of degraded image is restored effectively, but the blurring of image edges and details receiving scattering effects cannot be remedied. This problem can be solved by multi-scale image fusion. In our method, we use Gaussian pyramid and Laplace pyramid images for fusion, and these two feature pyramids are constructed by using the image after the illumination adjustment. The experimental results illustrate that dark-area sensitivity, global contrast, and edge sharpness of images have been improved by our methods. Moreover, in terms of visual effects and assessment metrics of underwater images, our method excels over other methods.
改进的色彩平衡和融合的水下图像增强
在水下摄影中,光的吸收和散射可能会导致对比度低、图像模糊和偏色。本文提出了一种有效的方法来改善被介质散射和吸收降低的水下图像质量。它将色彩补偿与多尺度图像融合相结合。色彩补偿包括对输入图像的$\mathbf{R}$通道和G-B通道的值进行独立校正,并调整校正后图像的白平衡。经过颜色补偿后,退化图像的颜色得到了有效的恢复,但无法弥补图像边缘和细节受到散射效应的模糊。多尺度图像融合可以解决这一问题。在我们的方法中,我们使用高斯金字塔和拉普拉斯金字塔图像进行融合,并使用光照调整后的图像构建这两个特征金字塔。实验结果表明,该方法提高了图像的暗区灵敏度、全局对比度和边缘清晰度。此外,在水下图像的视觉效果和评估指标方面,我们的方法优于其他方法。
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