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 https://github.com/bywlzts/CWNet-Causal-Wavelet-Network.

In recent years, infrared small target detection (ISTD) networks based on deep learning have achieved notable advances. However, these methods still face significant challenges when applied to the real world. Most of them lack the fundamental principles of small target detection in infrared imagery, which results in difficulties in distinguishing targets from complex backgrounds and poor interpretability. To address these challenges, an interpretable network architecture for ISTD, termed low-rank sparse decomposition state space models (LSDSSMs), is proposed. LSDSSMs use the principles of low-rank and sparse decomposition, incorporating dedicated modules for the low-rank space separation module and the sparse target extraction module. These modules facilitate the extraction of sparse representations for both low-rank backgrounds and small targets. In addition, a joint reconstruction module is employed to integrate these components, generating reconstructed images. Considering the unique imaging characteristics of infrared images and the sparse nature of small targets, a channel selection module (CSM) is proposed to enhance the extraction of sparse targets. To enhance the adaptability, stability, and resistance resistance of LSDSSMs in complex environments, robust state space models are integrated that combine local and global information representations. Furthermore, a multilevel loss function is introduced to enforce comprehensive constraints on low-rank backgrounds, sparse targets, and reconstructed images. This design improves not only the robustness of the LSDSSMs but also its performance across different scenarios. Extensive experimental results demonstrate that LSDSSMs surpass existing baseline methods in both qualitative and quantitative assessments, validating their effectiveness and reliability.