mlsanityData to 14 Oct 2026

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

arXiv 2304.03171 ↗First posted 6 Apr 2023eess.IV cs.CV cs.LGcited by 0

Not on the map. Why?

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

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

7 of 15 references.

References · 7

  1. 2021Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescuecited by 72
  2. 2019Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depthcited by 50
  3. 2021Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusioncited by 17
  4. 2020Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learningcited by 13
  5. 2021Lighting Enhancement Aids Reconstruction of Colonoscopic Surfacescited by 8
  6. 2022Multi-Scale Structural-aware Exposure Correction for Endoscopic Imagingcited by 6
  7. 2022A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Modelscited by 3

Built on by

Not cited yet.