Abstract
The concept of waterbody style transfer remains largely unexplored in the underwater imaging and vision literature. Traditional image style transfer (STx) methods primarily focus on artistic and photorealistic blending, often failing to preserve object and scene geometry in images captured in high-scattering mediums such as underwater. The wavelength-dependent nonlinear attenuation and depth-dependent backscattering artifacts further complicate learning underwater image STx from unpaired data. This paper introduces UStyle, the first data-driven learning framework for transferring waterbody styles across underwater images without requiring prior reference images or scene information. We propose a novel depth-aware whitening and coloring transform (DA-WCT) mechanism that integrates physics-based waterbody synthesis to ensure perceptually consistent stylization while preserving scene structure. To enhance style transfer quality, we incorporate carefully designed loss functions that guide UStyle to maintain colorfulness, lightness, structural integrity, and frequency-domain characteristics, as well as high-level content in VGG and CLIP (contrastive language-image pretraining) feature spaces. By addressing domain-specific challenges, UStyle provides a robust framework for no-reference underwater image STx, surpassing state-of-the-art (SOTA) methods that rely solely on end-to-end reconstruction loss. Furthermore, we introduce the UF7D dataset, a curated collection of high-resolution underwater images spanning seven distinct waterbody styles, establishing a benchmark to support future research in underwater image STx. The UStyle inference pipeline and UF7D dataset are released at: this https URL.
Abstract (translated)
水体风格转换的概念在水下成像和视觉文献中仍然未被充分探索。传统图像风格转移（STx）方法主要集中在艺术性和逼真的融合上，往往无法保留高散射介质如水下环境中捕捉到的物体和场景几何结构。波长依赖性非线性衰减以及深度相关的背向散射伪影进一步复杂化了从未配对数据中学习水下图像风格转移的过程。本文介绍了UStyle，这是首个无需先验参考图像或场景信息即可实现跨水下图像间水体风格转换的数据驱动式学习框架。我们提出了一种新颖的基于深度感知的白化和着色变换（DA-WCT）机制，并将其与基于物理原理的水体合成相结合，以确保在保持场景结构的同时进行感知一致的风格化处理。为了提升风格转移的质量，我们设计了一系列精心挑选的损失函数，指导UStyle保留色彩丰富度、亮度、结构性质及频域特性，并且在VGG和CLIP（对比语言-图像预训练）特征空间中维持高层次的内容信息。通过解决特定领域的挑战，UStyle为无参考水下图像风格转移提供了一个稳健的框架，超越了仅依赖端到端重建损失的当前最先进技术。此外，我们还引入了UF7D数据集，这是一个精心挑选、涵盖七种不同水体样式的高分辨率水下图片集合，它建立了基准以支持未来在水下图像风格转换领域的研究。UStyle推理管线和UF7D数据集可在以下网址获取：此链接。
URL
https://arxiv.org/abs/2503.11893
