基于颜色-场景联合迁移的水下图像增强方法

Joint Color and Scene Adaptation for Underwater Image Enhancement

摘要:

水下成像存在颜色畸变、图像对比度严重下降等问题。大多数基于深度学习的水下图像增强方法依赖仿真数据集,由于仿真与实测数据之间存在较大的分布差异,实测泛化能力受限。本文将水下图像增强任务划分为两个更简单但是同时具有明确物理意义的子问题：颜色校正和对比度增强,提出基于物理模型分解的域内-域间迁移框架。首先,域内迁移校正图像颜色,通过学习对退化图像进行分解,在场景光层面通过对齐颜色退化,校正颜色畸变同时保证其他成分完全不受影响。进一步,再次利用基于水下散射模型的分解策略,通过针对性迁移水下退化因素,使得仿真-实测域之间实现相互迁移和交互,增强水下图像对比度。实验结果表明,本方法在真实水下图像数据集上处理的结果,在色彩、纹理细节和清晰程度方面均优于现有的对比方法。

关键词:图像处理;水下图像增强;卷积神经网络;域迁移

Abstract:

Underwater images suffer from the degradations such as color distortion and low-contrast, severely decreasing the image quality. The existing underwater image enhancement methods heavily rely on synthetic data and may face difficulties to generalize well in real scenes, due to the huge domain gap between synthetic and real images. To solve the problem, we formulate the challenging underwater image enhancement task into two easier yet physical meaning sub-problems: color correction and contrast enhancement. We propose a physically disentangled joint intra- and inter-domain adaptation paradigm, in which intra-domain adaptation focuses on color correction and inter-domain procedure transfers knowledge between synthetic and real domains. We first learn to physically disentangle haze images into three components complying with the scattering model: background, transmission map, and atmospheric light. Since color distortion is determined by scene light, we perform intra-domain adaptation by specifically translating scene light from varicolored space to unified color-balanced space, correcting the color distortion. Consequently, we perform inter-domain adaptation between the synthetic and real images by mutually exchanging the background and other two components. Extensive experiments demonstrate the superiority of the proposed method over the state-of-the-art for real varicolored water image enhancement.

Key words:image processing; underwater image enhancement; CNN; domain adaptation