通过位平面分解挖掘低光图像内在信息，实现无监督高效增强。
SIMI: Self-information Mining Network for Low-light Image Enhancement
- 基于位平面分解，从低光图像中自适应提取内在特征。
- 无需外部数据，在标准测试集上达到顶尖性能。
- 无监督设计适合真实场景应用，计算开销小。
光照不足严重影响图像质量，给图像编辑与可视化带来挑战。现有增强方法多依赖复杂模型，忽视低光图像自身的内在信息。本文提出自信息挖掘（SIMI）网络，一种创新的无监督框架，通过位平面分解将低光图像拆分为多个成分，从而在不依赖外部数据的情况下挖掘内在信息。该方法不仅加速模型收敛，还提升性能并降低计算开销。无监督特性增强了实际应用能力。在标准基准上的实验表明，SIMI实现了当前最优性能。
原文摘要 · Abstract (English)
Poor lighting conditions significantly impact image quality, posing substantial challenges for image editing and visualization. Many existing enhancement methods aim at proposing complex models while neglecting the intrinsic information contained within low-light images. In this work, we propose the Self-Information Mining (SIMI) network, an innovative unsupervised framework that decomposes low-light images into multiple components based on bit-plane decomposition. Our approach allows mining intrinsic information without relying on external data. This not only accelerates model convergence but also improves performance and reduces computational overhead. The unsupervised nature of our method facilitates real-world applicability. Experiments conducted on standard benchmarks demonstrate that SIMI achieves state-of-the-art performance.
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