  Deep learning models have witnessed depth and pose estimation framework on
unannotated datasets as a effective pathway to succeed in endoscopic
navigation. Most current techniques are dedicated to developing more advanced
neural networks to improve the accuracy. However, existing methods ignore the
special properties of endoscopic images, resulting in an inability to fully
unleash the power of neural networks. In this study, we conduct a detail
analysis of the properties of endoscopic images and improve the compatibility
of images and neural networks, to unleash the power of current neural networks.
First, we introcude the Mask Image Modelling (MIM) module, which inputs partial
image information instead of complete image information, allowing the network
to recover global information from partial pixel information. This enhances the
network' s ability to perceive global information and alleviates the phenomenon
of local overfitting in convolutional neural networks due to local artifacts.
Second, we propose a lightweight neural network to enhance the endoscopic
images, to explicitly improve the compatibility between images and neural
networks. Extensive experiments are conducted on the three public datasets and
one inhouse dataset, and the proposed modules improve baselines by a large
margin. Furthermore, the enhanced images we proposed, which have higher network
compatibility, can serve as an effective data augmentation method and they are
able to extract more stable feature points in traditional feature point
matching tasks and achieve outstanding performance.
