Hybrid Feature Edge Enhancement for Self-Supervised Monocular Depth Estimation in Endoscopic Scenes

Reliable monocular depth prediction from endoscopic video frames remains useful for scale-aware scene understanding during minimally invasive procedures and supports applications including surgical 3D reconstruction and intraoperative navigation guidance. However, self-supervised methods based on photometric reconstruction often become unstable in weakly textured tissue regions and near poorly separated tissue interfaces, which may cause blurred depth transitions and distorted local geometry. To mitigate these limitations, we design a boundary-oriented self-supervised endoscopic depth model with hybrid edge-feature enhancement. The proposed model improves boundary detail preservation and local structural consistency by strengthening tissue-boundary-sensitive representations and enhancing cross-scale decoder fusion. Specifically, it consists of a Hybrid Feature Edge Enhancement Module (HFE-EM) and a Multi-Feature Fusion Convolutional Block (MFCB). HFE-EM incorporates complementary edge and texture cues into encoder features to improve boundary-aware representation learning, while MFCB refines decoder-side feature aggregation under global contextual guidance. Experiments on the SCARED dataset, cross-dataset evaluation using Hamlyn, and ablation studies show that our method obtains competitive performance and good generalization ability for depth estimation in endoscopic scenes.