基于颜色校正的水下照明图像融合方法

Underwater Illumination Image Fusion Method Based on Color Correction

  • 摘要: 针对水下照明图像存在不均匀色偏、对比度低和细节模糊等问题,提出了一种基于颜色校正的水下照明图像融合方法。首先利用图像通道间的像素相关性,对红通道进行补偿;然后基于颜色校正图像,利用非线性反锐化掩蔽(Nonlinear unsharp masking)技术获得锐度增强图像,采用具有瑞利分布的限制直方图获得全局拉伸图;最后通过多尺度融合策略生成融合图像。在自建数据集(Real underwater lighting image,RULI)上的实验结果表明:本文方法能够去除混合光照在成像过程中的不均匀散射干扰,并大幅度提高图像的细节清晰度。其图像质量评估指标(Underwater image quality measures,UIQM)和(Image entropy,IE)的平均值分别为4.7399和7.7617,优于现有文献涉及的相关算法。

     

    Abstract: We proposed a color-corrected underwater illumination image fusion method based on color correction to address uneven color shifts, low contrast, and blurred details in underwater illumination images. First, we exploited the pixel correlation between image channels to compensate for the red channel. Then, based on the color-corrected image, a sharpness-enhanced image is obtained using a nonlinear unsharp masking technique, and a global stretching map is obtained using a restricted histogram with Rayleigh distribution. Finally, we generated the fused image using a multi-scale fusion strategy. The experimental results on a self-built dataset (RULI) showed that the proposed method could remove the inhomogeneous scattering interference of mixed illumination in the imaging process and substantially improve the detail sharpness of the image. The mean values of the image quality assessment metrics UIQM and IE were 4.7399 and 7.7617, respectively, better than those of related algorithms in the existing literature.

     

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