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Related Experiment Video

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EIEN: Endoscopic Image Enhancement Network Based on Retinex Theory.

Ziheng An1,2, Chao Xu1,2, Kai Qian1,2

  • 1School of Integrated Circuits, Anhui University, Hefei 230601, China.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

A novel deep learning network, Endoscopic Image Enhancement Network (EIEN), effectively improves medical endoscopic images by enhancing brightness, contrast, and vascular details using Retinex theory and self-attention mechanisms.

Keywords:
convolutional neural networkendoscopic imageimage enhancementretinexself-attention mechanism

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Deep convolutional neural networks (CNNs) show promise for image enhancement.
  • Medical endoscopic images often suffer from uneven illumination and low contrast, posing challenges for CNN-based enhancement.

Purpose of the Study:

  • To propose a novel deep learning network, Endoscopic Image Enhancement Network (EIEN), for improving medical endoscopic images.
  • To address challenges of uneven illumination and low contrast in endoscopic imaging.

Main Methods:

  • EIEN utilizes Retinex theory, decomposing images into illumination and reflection components.
  • A self-attention guided multi-scale pyramid structure corrects illumination components.
  • Sub-channel stretching and weighted fusion enhance reflection components, highlighting vascular information.

Main Results:

  • The proposed EIEN method demonstrated superior performance compared to six other methods.
  • EIEN successfully enhanced brightness and contrast in endoscopic images.
  • Vascular and tissue information within the images were significantly highlighted.

Conclusions:

  • EIEN effectively enhances medical endoscopic images, improving visual perception and objective evaluation.
  • The network's architecture, combining Retinex theory with self-attention, is well-suited for endoscopic image enhancement.