# Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction

arXiv: https://arxiv.org/abs/2304.03171 · HTML: https://mlsanity.com/paper/2304.03171/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue](https://mlsanity.com/paper/2112.08122/) (2021)
- [Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth](https://mlsanity.com/paper/1904.07087/) (2019)
- [Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion](https://mlsanity.com/paper/2111.04911/) (2021)
- [Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning](https://mlsanity.com/paper/2011.07631/) (2020)
- [Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces](https://mlsanity.com/paper/2103.10310/) (2021)
- [Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging](https://mlsanity.com/paper/2210.15033/) (2022)
- [A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models](https://mlsanity.com/paper/2207.02396/) (2022)

## Abstract (arXiv)

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