Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction
This work addresses a domain-specific problem for medical imaging and colonoscopy, but it is incremental as it focuses on pre-processing enhancements rather than a novel reconstruction method.
The paper tackled the problem of inaccurate 3D reconstruction in colonoscopy by applying deep learning-based local image exposure correction as a pre-processing step, resulting in improved reconstruction accuracy of the endoscope trajectory.
This contribution shows how an appropriate image pre-processing can improve a deep-learning based 3D reconstruction of colon parts. The assumption is that, rather than global image illumination corrections, local under- and over-exposures should be corrected in colonoscopy. An overview of the pipeline including the image exposure correction and a RNN-SLAM is first given. Then, this paper quantifies the reconstruction accuracy of the endoscope trajectory in the colon with and without appropriate illumination correction