基于场景深度估计和背景分割的水下图像复原

Underwater Image Restoration Based on Scene Depth Estimation and Background Segmentation

针对水下图像对比度低、颜色失真、可见度低等问题,提出了一种基于场景深度估计和背景区域分割的复原方法。首先,利用多方向斜梯度算子和各颜色通道的衰减差估计图像的场景深度。然后,利用场景深度估计过程中得到的梯度和色差信息将图像的背景与前景区域分离,并分别在背景和前景区域估计背景光和透射率。在得到背景光和透射率图后,基于水下成像模型对前景区域进行场景恢复,同时采用在HSV颜色空间直方图拉伸的方法对背景区域进行对比度增强。最后,通过设置过渡区域权重图对前景和背景进行融合得到最终的复原结果。实验结果表明,所提方法能更准确地估计背景光和透射率,在对比度增强、色彩修正及清晰度提升等方面具有良好的性能。与经典的方法对比,所提方法在UIQM、UCIQE、FDUM和FADE等4个客观质量评价指标上的提升均超过15%。

Underwater images often suffer from low contrast,color distortion,and poor visibility.To solve these problems,herein a novel underwater image restoration method based on scene depth estimation and background segmentation is proposed.First,the scene depth is estimated using multiple oblique gradient operators and attenuation difference among color channels.Then,according to the image gradient and color difference information,the degraded underwater image is divided into the foreground region and the background region.Accordingly,the background light(BL)is estimated in the background region and transmission maps are obtained using the estimated scene depth map.Subsequently,the scene radiance of the foreground region is recovered based on the underwater image formation model,and the background region is enhanced by performing histogram stretching in the HSV color space.Finally,the foreground and background are fused using a weight map of the transition region to obtain the final restoration result.Experimental results show that the proposed method can estimate the background light and transmittance with significantly greater accuracy,and achieves satisfactory contrast enhancement,color correction,and sharpness improvement.Compared with several classical methods,the proposed method affords 15% better performance on average in terms of the following four image quality evaluation metrics:UIQM,UCIQE,FDUM,and FADE.