IDM-Net：一种用于低光图像增强的轻量级光照解耦调制网络

中文摘要 AI 辅助

低光图像增强（LLIE）对于轻量级模型而言仍然具有挑战性，因为在RGB颜色空间中，光照恢复和颜色保真难以同时优化。尽管最近的色彩解耦方法分离了亮度和色度表示，但它们主要将亮度作为增强目标进行优化，在特征重建过程中，其作为显式引导先验的潜力在很大程度上未被探索。为了解决这一局限性，我们提出了IDM-Net，一种用于低光图像增强的轻量级光照解耦调制网络。IDM-Net采用双编码器架构，包括一个从RGB图像中提取多尺度外观特征的结构编码器，以及一个从解耦的亮度（Y）通道中学习光照先验的轻量级光照编码器。为了有效利用这些先验，我们引入了一个光照引导调制（IGM）模块，该模块通过空间自适应仿射调制将多尺度光照线索注入解码器，从而在保持自然色彩一致性的同时实现准确的亮度恢复。此外，我们设计了一个轻量级特征细化块（FRB），在重建过程中逐步抑制退化伪影并恢复细粒度图像细节。在多个标准低光图像增强基准上的大量实验表明，IDM-Net在轻量级LLIE方法中取得了具有竞争力的性能，同时在恢复质量和计算效率之间保持了良好的平衡。

英文摘要

Low-light image enhancement (LLIE) remains challenging for lightweight models because illumination restoration and color fidelity are difficult to optimize simultaneously in the RGB color space. Although recent color-decoupled methods separate luminance and chrominance representations, they primarily optimize luminance as an enhancement target, leaving its potential as an explicit guidance prior largely unexplored during feature reconstruction. To address this limitation, we propose IDM-Net, a lightweight Illumination-Decoupled Modulation Network for low-light image enhancement. IDM-Net adopts a dual-encoder architecture consisting of a structure encoder that extracts multi-scale appearance features from the RGB image and a lightweight illumination encoder that learns illumination priors from the decoupled luminance (Y) channel. To effectively exploit these priors, we introduce an Illumination-Guided Modulation (IGM) module that injects multi-scale illumination cues into the decoder through spatially adaptive affine modulation, enabling accurate brightness restoration while preserving natural color consistency. Furthermore, we design a lightweight Feature Refinement Block (FRB) to progressively suppress degradation artifacts and recover fine-grained image details during reconstruction. Extensive experiments on multiple standard low-light image enhancement benchmarks demonstrate that IDM-Net achieves competitive performance among lightweight LLIE methods while maintaining an excellent balance between restoration quality and computational efficiency.

发表机构

• National Taiwan University of Science and Technology（国立台湾科技大学）

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