解决内镜图像光照不均导致的深度估计不准问题
DeLightMono: Enhancing Self-Supervised Monocular Depth Estimation in Endoscopy by Decoupling Uneven Illumination
- 将光照、反射与深度解耦建模，分步优化
- 在两个公开数据集上显著提升低光区域深度精度
- 适合内镜导航系统开发人员参考使用
自监督单目深度估计是内镜导航系统的关键任务。然而，内镜图像固有的光照不均，尤其在低照度区域，导致性能下降。现有低光增强技术无法有效指导深度网络，而其他领域（如自动驾驶）的方法需良好光照条件，不适用且增加数据采集负担。为此，我们提出DeLight-Mono——一种具有光照解耦能力的自监督单目深度估计框架。具体地，设计了光照-反射-深度模型表示内镜图像，并通过辅助网络进行分解。此外，提出基于解耦成分的自监督联合优化框架及新损失函数，缓解光照不均对深度估计的影响。在两个公开数据集上通过大量对比实验与消融研究验证了方法的有效性。
原文摘要 · Abstract (English)
Self-supervised monocular depth estimation serves as a key task in the development of endoscopic navigation systems. However, performance degradation persists due to uneven illumination inherent in endoscopic images, particularly in low-intensity regions. Existing low-light enhancement techniques fail to effectively guide the depth network. Furthermore, solutions from other fields, like autonomous driving, require well-lit images, making them unsuitable and increasing data collection burdens. To this end, we present DeLight-Mono - a novel self-supervised monocular depth estimation framework with illumination decoupling. Specifically, endoscopic images are represented by a designed illumination-reflectance-depth model, and are decomposed with auxiliary networks. Moreover, a self-supervised joint-optimizing framework with novel losses leveraging the decoupled components is proposed to mitigate the effects of uneven illumination on depth estimation. The effectiveness of the proposed methods was rigorously verified through extensive comparisons and an ablation study performed on two public datasets.
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