Image recovery through turbid water under wide distance ranges

Lina Zhou, Yin Xiao, Wen Chen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


Imaging through scattering media is a long-standing problem which has been extensively studied to promote the development of imaging in complex environments. Extant techniques for image reconstruction in scattering media face with the disadvantages of limited ranges of applications, high sensitivity to environmental changes and huge computational load. The scattering media commonly used in practical applications are more complicated due to unknown perturbations. One of the most outstanding problems is the uncertainty of the object position which obstructs progressive development of image recovery techniques. Therefore, it is meaningful to explore a feasible method to bypass additional requirements of precision measuring instruments. Here, we present a method based on convolution neural network (CNN) for optical image reconstruction. The targets are placed in the scattering media which are composed of a certain volume of water and milk, and their diffraction patterns are recorded by using a camera. The learning model demonstrated in this paper is tolerant to uncertainty of object positions. It is foreseeable to be a promising substitute for imaging objects in harsh environments.

Original languageEnglish
Title of host publicationSeventh International Conference on Optical and Photonic Engineering, icOPEN 2019
EditorsAnand Asundi, Qian Kemao
ISBN (Electronic)9781510631595
Publication statusPublished - 1 Jan 2019
Event7th International Conference on Optical and Photonic Engineering, icOPEN 2019 - Phuket, Thailand
Duration: 16 Jul 201920 Jul 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference7th International Conference on Optical and Photonic Engineering, icOPEN 2019


  • Image reconstruction
  • Imaging through turbid water
  • Machine learning
  • Underwater imaging

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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