CIVQLLIE: Causal Intervention with Vector Quantization for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2508.03338v1
• Date: Tue, 05 Aug 2025 11:36:39 GMT
• Title: CIVQLLIE: Causal Intervention with Vector Quantization for Low-Light Image Enhancement
• Authors: Tongshun Zhang, Pingping Liu, Zhe Zhang, Qiuzhan Zhou,
• Abstract summary: Current low-light image enhancement methods face significant challenges.<n>We propose CIVQLLIE, a novel framework that leverages the power of discrete representation learning through causal reasoning.
• Score: 5.948286668586509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Images captured in nighttime scenes suffer from severely reduced visibility, hindering effective content perception. Current low-light image enhancement (LLIE) methods face significant challenges: data-driven end-to-end mapping networks lack interpretability or rely on unreliable prior guidance, struggling under extremely dark conditions, while physics-based methods depend on simplified assumptions that often fail in complex real-world scenarios. To address these limitations, we propose CIVQLLIE, a novel framework that leverages the power of discrete representation learning through causal reasoning. We achieve this through Vector Quantization (VQ), which maps continuous image features to a discrete codebook of visual tokens learned from large-scale high-quality images. This codebook serves as a reliable prior, encoding standardized brightness and color patterns that are independent of degradation. However, direct application of VQ to low-light images fails due to distribution shifts between degraded inputs and the learned codebook. Therefore, we propose a multi-level causal intervention approach to systematically correct these shifts. First, during encoding, our Pixel-level Causal Intervention (PCI) module intervenes to align low-level features with the brightness and color distributions expected by the codebook. Second, a Feature-aware Causal Intervention (FCI) mechanism with Low-frequency Selective Attention Gating (LSAG) identifies and enhances channels most affected by illumination degradation, facilitating accurate codebook token matching while enhancing the encoder's generalization performance through flexible feature-level intervention. Finally, during decoding, the High-frequency Detail Reconstruction Module (HDRM) leverages structural information preserved in the matched codebook representations to reconstruct fine details using deformable convolution techniques.

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