Di$^2$CycleSB：基于薛定谔桥Transformer的高质量无监督夜间能见度增强方法
Di$^2$CycleSB: Towards High-Quality Unsupervised Nighttime Visibility Enhancement via Schrödinger Bridge Transformer
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中文总结 AI 辅助
本研究提出Di$^2$CycleSB框架，将光效抑制建模为薛定谔桥问题，结合Transformer与动态积分图像先验，在无监督场景下实现高质量夜间能见度增强，实验验证其有效性。
中文摘要 AI 辅助
光效污染是夜间能见度增强领域面临的重大挑战。现有多数方法通过基于先验的正则化来估计并分解光效，然而这类方法常受限于人工设计的先验以及分解问题的不适定性。本研究提出Di$^2$CycleSB，这是一个由动态积分图像先验引导的无监督Cycle薛定谔桥Transformer框架，用于实现高质量的无监督夜间能见度增强。具体而言，我们引入一种新型光效估计器，通过聚合动态积分图像表示来参数化类高斯自适应先验，以实现非均匀辉光估计。随后，我们提出一种先验感知生成器，其利用光效表示在特定Transformer块内建模长距离依赖关系。我们将光效抑制问题建模为薛定谔桥问题，并构建带有循环一致性约束的前向与后向桥，以实现视觉效果良好的增强。在真实世界数据集上开展的大量实验表明，Di$^2$CycleSB在夜间能见度增强方面具备显著有效性，尤其可在无需任何正则化约束与图像分解的情况下实现端到端的有效光效抑制。代码与模型可在该httpsURL获取。
英文摘要
Light-effect contamination poses a significant challenge to nighttime visibility enhancement. Most methods suppress light effects by estimating and decomposing them through prior-driven regularization, yet they are often limited by hand-crafted priors and ill-posed nature of decomposition. This work proposes Di$^2$CycleSB, a unsupervised Cycle Schrödinger Bridge Transformer framework guided by dynamic integral image priors, for high-quality unsupervised nighttime visibility enhancement. Specifically, a novel light-effect estimator is introduced to parameterize Gaussian-like adaptive priors by aggregating dynamic integral image representations for non-uniform glow estimation. Then, we propose a prior-informed Generator that exploits light-effect representations to guide long-range dependency modeling within our specific Transformer blocks. We formulate light-effect suppression as a Schrödinger bridge problem and construct forward and backward bridges with cycle consistency constraints to achieve visually pleasing enhancement. Extensive experiments on real-world datasets demonstrate the remarkable effectiveness of our Di$^2$CycleSB in enhancing nighttime visibility. In particular, it achieves effective end-to-end light-effect suppression without any regularization constraints and image decomposition. The code and models are available at https://github.com/LHTcode/Di2CycleSB.
发表机构
- Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）
- Department of Automation, Tsinghua University（清华大学自动化系）
- College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）
机构由 AI 辅助整理，请以论文原文为准。
