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Denoising and contrast enhancement fusion based on white balance for underwater images

机译:基于白平衡的白平衡的去噪与对比增强融合

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When the scene reached the human eye or other sensor through the underwater medium, the original color property of the object could be basically lost, and the background of most images was blue-green. In this paper, to remove the blue-green color of the underwater image and increase the color contrast of the image, a novel fusion method of two images was proposed. The two images were derived from the filtering denoising result and the contrast enhancement result after whitebalancing version of the original degraded image. Then, based on the two images, the associated weight maps were designed to enhance edge texture and color contrast of the output image. Finally, to avoid artifacts in the reconstructed image, we adopted the multiscale fusion strategy to fuse the processed two images. Experiments showed that our algorithm achieved better image contrast and edge sharpness than other methods and obtained better exposure for darker areas of the image.
机译:当场景通过水下介质到达人眼或其他传感器时,物体的原始颜色属性基本上可能是丢失的,并且大多数图像的背景是蓝绿色的。在本文中,为了去除水下图像的蓝绿色并增加图像的颜色对比度,提出了一种三种图像的新融合方法。从滤波去噪结果中导出了两个图像,并且在WhiteBalancing版本的原始降级图像之后的对比增强结果。然后,基于两个图像,设计相关的权重映射以增强输出图像的边缘纹理和颜色对比度。最后,为了避免重建图像中的文物,我们采用了多尺度融合策略来融合处理的两个图像。实验表明,我们的算法比其他方法实现了更好的图像对比度和边缘清晰度,并获得了更好的图像曝光区域。

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