# Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces

arXiv: https://arxiv.org/abs/2103.10310 · HTML: https://mlsanity.com/paper/2103.10310/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction](https://mlsanity.com/paper/1704.03489/) (2017)
- [Guided Collaborative Training for Pixel-wise Semi-Supervised Learning](https://mlsanity.com/paper/2008.05258/) (2020)
- [Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry](https://mlsanity.com/paper/1807.02570/) (2018)

## Built on by (most cited first, 8 of 8)

- [Surgical Tattoos in Infrared: A Dataset for Quantifying Tissue Tracking and Mapping](https://mlsanity.com/paper/2309.16782/) (2023)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [A Surface-normal Based Neural Framework for Colonoscopy Reconstruction](https://mlsanity.com/paper/2303.07264/) (2023)
- [ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction](https://mlsanity.com/paper/2111.10371/) (2021)
- [C3Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy](https://mlsanity.com/paper/2206.01961/) (2022)
- [CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy](https://mlsanity.com/paper/2206.14951/) (2022)
- [A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models](https://mlsanity.com/paper/2207.02396/) (2022)
- [Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction](https://mlsanity.com/paper/2304.03171/) (2023)

## Abstract (arXiv)

High screening coverage during colonoscopy is crucial to effectively prevent colon cancer. Previous work has allowed alerting the doctor to unsurveyed regions by reconstructing the 3D colonoscopic surface from colonoscopy videos in real-time. However, the lighting inconsistency of colonoscopy videos can cause a key component of the colonoscopic reconstruction system, the SLAM optimization, to fail. In this work we focus on the lighting problem in colonoscopy videos. To successfully improve the lighting consistency of colonoscopy videos, we have found necessary a lighting correction that adapts to the intensity distribution of recent video frames. To achieve this in real-time, we have designed and trained an RNN network. This network adapts the gamma value in a gamma-correction process. Applied in the colonoscopic surface reconstruction system, our light-weight model significantly boosts the reconstruction success rate, making a larger proportion of colonoscopy video segments reconstructable and improving the reconstruction quality of the already reconstructed segments.