论文标题
通过内容和样式分离的水下图像增强的域适应
Domain Adaptation for Underwater Image Enhancement via Content and Style Separation
论文作者
论文摘要
水下图像由于光吸收，折射率和散射而受到彩色铸造，低对比度和朦胧效应，从而降低了高级应用，例如，对象检测和对象跟踪。最新的基于学习的方法证明了水下图像增强的惊人性能，但是，这些作品中的大多数都使用合成对数据进行监督学习，而忽略了对现实世界数据的域间隙。要解决这个问题，我们提出了一个通过内容和样式分离来增强水下图像的领域适应框架，与先前的域适应性作品不同的水下图像增强作品，该框架的目标是最小化综合和现实数据的潜在差异，我们旨在将其分开的特征和样式的延伸范围分开，并将其分开，并将其分开，并将其分开。域，以及潜在空间中的过程域的适应和图像增强。通过潜在的操作，我们的模型提供了用户交互接口，以调整不同的增强级别以进行连续更改。对各种公共水下基准测试的实验表明，所提出的框架能够对水下图像增强的域进行适应性，并且在数量和质量方面表现优于各种最新的水下图像增强算法。该型号和源代码将在https://github.com/fordevoted/uiess上找到
Underwater image suffer from color cast, low contrast and hazy effect due to light absorption, refraction and scattering, which degraded the high-level application, e.g, object detection and object tracking. Recent learning-based methods demonstrate astonishing performance on underwater image enhancement, however, most of these works use synthetic pair data for supervised learning and ignore the domain gap to real-world data. To solve this problem, we propose a domain adaptation framework for underwater image enhancement via content and style separation, different from prior works of domain adaptation for underwater image enhancement, which target to minimize the latent discrepancy of synthesis and real-world data, we aim to separate encoded feature into content and style latent and distinguish style latent from different domains, i.e. synthesis, real-world underwater and clean domain, and process domain adaptation and image enhancement in latent space. By latent manipulation, our model provide a user interact interface to adjust different enhanced level for continuous change. Experiment on various public real-world underwater benchmarks demonstrate that the proposed framework is capable to perform domain adaptation for underwater image enhancement and outperform various state-of-the-art underwater image enhancement algorithms in quantity and quality. The model and source code will be available at https://github.com/fordevoted/UIESS
