EnlightenDepth: a Novel Self-supervised Monocular Depth Estimation with Low-light Enhancement in Endoscopy

In minimally invasive surgery, depth estimation is important for enhancing the perceptual abilities of surgeons. Current monocular self-supervised depth estimation methods suffer from the problems of poor illumination and narrow-view field in endoscopy. To address them, we propose a novel self-supervised monocular depth estimation with low-light enhancement, named EnlightenDepth. First, we introduce a GAN-based module to improve the local brightness and visibility of endoscopic images. Then, we leverage the appearance flow to handle the illumination variance between the adjacent image frames, assisting the supervision. Furthermore, to better extract global and local features, we apply MonoVit to combine the advantages of CNN and ViT. Extensive experiments are conducted on the Hamlyn dataset, proving that low-light enhancement can improve depth estimation performance in endoscopic illumination conditions. Compared with other monocular self-supervised methods designed for endoscopy, our method achieves around 10% improvement.