Abstract
Traditional Low-Light Image Enhancement (LLIE) methods primarily focus on uniform brightness adjustment, often neglecting instance-level semantic information and the inherent characteristics of different features. To address these limitations, we propose CWNet (Causal Wavelet Network), a novel architecture that leverages wavelet transforms for causal reasoning. Specifically, our approach comprises two key components: 1) Inspired by the concept of intervention in causality, we adopt a causal reasoning perspective to reveal the underlying causal relationships in low-light enhancement. From a global perspective, we employ a metric learning strategy to ensure causal embeddings adhere to causal principles, separating them from non-causal confounding factors while focusing on the invariance of causal factors. At the local level, we introduce an instance-level CLIP semantic loss to precisely maintain causal factor consistency. 2) Based on our causal analysis, we present a wavelet transform-based backbone network that effectively optimizes the recovery of frequency information, ensuring precise enhancement tailored to the specific attributes of wavelet transforms. Extensive experiments demonstrate that CWNet significantly outperforms current state-of-the-art methods across multiple datasets, showcasing its robust performance across diverse scenes. Code is available at this https URL.
Abstract (translated)
传统低光图像增强（LLIE）方法主要集中在均匀亮度调整上，往往忽视了实例级别的语义信息以及不同特征的固有特性。为了克服这些限制，我们提出了CWNet（因果小波网络），这是一种利用小波变换进行因果推理的新架构。 具体来说，我们的方法包含两个关键组件： 1. **因果推理解析**：借鉴因果关系中的干预概念，我们采用一种因果推理视角来揭示低光增强中潜在的因果关系。从全局角度来看，我们采用了度量学习策略以确保因果嵌入符合理论因果原则，并且能够将它们与非因果混淆因素区分开来，同时关注于因果因素的不变性。在局部层面，我们引入了一种实例级别的CLIP语义损失，用于精确保持因果因素的一致性。 2. **基于小波变换的骨干网络**：根据我们的因果分析结果，我们提出了一种基于小波变换的骨干网络，该网络能够有效地优化频率信息的恢复过程，并确保增强效果精准地符合特定的小波变换属性。 广泛的实验表明，在多个数据集上CWNet显著优于当前最先进的方法，展示了其在各种场景下的稳健性能。代码可在以下链接获取：[此URL](https://this-url.com)（请注意将"[此URL]"替换为实际的GitHub或项目页面地址）。
URL
https://arxiv.org/abs/2507.10689
