轻量级暗光图像增强，兼顾速度与画质
Self-DACE++: Robust Low-Light Enhancement via Efficient Adaptive Curve Estimation
- 用极少参数的自适应曲线动态调整亮度范围
- 在多个真实数据集上超越现有方法，支持实时推理
- 适合移动端或嵌入式设备的低光图像处理
本文提出 Self-DACE++，一种改进的无监督轻量级暗光图像增强框架，基于先前的 Self-Reference Deep Adaptive Curve Estimation（Self-DACE）。为更好平衡计算效率与恢复质量，Self-DACE++ 引入增强型自适应调整曲线（AACs），仅需极少可训练参数，即可灵活调节动态范围，同时保持色彩保真度、结构完整性和自然性。为实现极轻量化架构而不损失性能，提出随机训练顺序策略与网络融合机制，将模型压缩为高效的迭代推理结构。此外，基于 Retinex 理论构建物理驱动的目标函数，并加入专用去噪模块，有效估计并抑制暗区中的潜在噪声。在多个真实世界基准数据集上的大量定性与定量评估表明，Self-DACE++ 在保持实时推理能力的同时，优于现有最先进方法，显著提升增强质量。代码已公开于 https://github.com/John-Wendell/Self-DACE。
原文摘要 · Abstract (English)
In this paper, we present Self-DACE++, an improved unsupervised and lightweight framework for Low-Light Image Enhancement (LLIE), building upon our previous Self-Reference Deep Adaptive Curve Estimation (Self-DACE). To better address the trade-off between computational efficiency and restoration quality, Self-DACE++ introduces enhanced Adaptive Adjustment Curves (AACs). These curves, governed by minimal trainable parameters, flexibly adjust the dynamic range while preserving the color fidelity, structural integrity, and naturalness of the enhanced images. To achieve an extremely lightweight architecture without sacrificing performance, we propose a randomized order training strategy coupled with a network fusion mechanism, which compresses the model into an efficient iterative inference structure. Furthermore, we formulate a physics-grounded objective function based on Retinex theory and incorporate a dedicated denoising module to effectively estimate and suppress latent noise in dark regions. Extensive qualitative and quantitative evaluations on multiple real-world benchmark datasets demonstrate that Self-DACE++ outperforms existing state-of-the-art methods, delivering superior enhancement quality with real-time inference capability. The code is available at https://github.com/John-Wendell/Self-DACE.
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