发表机构
Central South University; Westlake University（中南大学; 西湖大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个全合一偏振图像恢复框架，采用双分支架构分离强度与偏振建模，利用通用恢复先验和专家混合处理复合退化，并通过跨域特征变换蒸馏知识，建立基准并验证有效性。
AI 中文摘要
偏振成像捕获了独特的表面和几何线索，这些线索有益于广泛的视觉任务。然而，真实世界的偏振采集常常受到多种耦合退化的影响，使得图像恢复对于实际的偏振视觉至关重要。现有方法大多针对特定退化进行定制，并且受限于偏振数据的有限规模和质量。为了解决这些限制，我们开发了一个用于多样化和复合退化的全合一偏振恢复框架。我们首先研究了不同偏振表示对恢复性能的影响，并确定了归一化斯托克斯表示作为分离强度与偏振信息的有效选择。据此，我们设计了一个双分支架构，将强度建模与偏振建模分离。为了克服偏振特定训练的限制，强度分支利用预训练的通用恢复先验和用于复合退化的专家混合扩展，而其恢复知识通过跨域特征变换自适应地蒸馏到对称的偏振分支中。此外，我们建立了一个复合退化偏振基准，以支持全合一恢复研究。在公共数据集和我们提出的基准上的大量实验证明了所提出方法的有效性。
英文摘要
Polarization imaging captures distinctive surface and geometric cues that benefit a wide range of vision tasks. However, real-world polarization acquisition is often affected by multiple coupled degradations, making image restoration essential for practical polarization vision. Existing methods are largely tailored to specific degradations and remain constrained by the limited scale and quality of polarization data. To address these limitations, we develop an all-in-one polarization restoration framework for diverse and composite degradations. We first study the impact of different polarization representations on restoration performance and identify the normalized Stokes representation as an effective choice for separating intensity and polarization information. Accordingly, we devise a dual-branch architecture that separates intensity and polarization modeling. To overcome the limitations of polarization-specific training, the intensity branch leverages pretrained general restoration priors and a mixture-of-experts extension for composite degradations, while its restoration knowledge is adaptively distilled into the symmetric polarization branch via a cross-domain feature transform. In addition, we establish a composite-degradation polarization benchmark to support all-in-one restoration research. Extensive experiments on public datasets and our proposed benchmark demonstrate the effectiveness of the proposed method.
