TANet: Transmission and atmospheric light driven enhancement of underwater images

Dehuan Zhang, Yakun Guo, Jingchun Zhou, Weishi Zhang, Zifan Lin, Kemal Polat, Fayadh Alenezi, Adi Alhudhaif

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Motivated by the adverse impact of light attenuation and scattering, which leads to color distortion and low contrast in underwater images, our study primarily focuses on enhancement techniques for these images using localized transmission feature analysis and global atmospheric light feature extraction. To this end, we propose a novel approach, named TANet, drawing upon the dynamics of transmission and atmospheric light. TANet integrates two primary components: a spatial domain-based Transmission-Driven Refinement module (TDR) and a frequency domain-based Atmospheric Light Removal Fourier Module (ALRF). The TDR module employs a Gated Multipurpose Unit with dual branches, selectively regulating input features. This allows for a refined merging of feature vectors that subsequently interact, enabling cross-channel feature integration. Capitalizing on the correlation between transmission and image quality, TDR facilitates the detailed enhancement of underwater images by depicting the perceived transmission across distinct image sections. Given that atmospheric light exhibits different attenuation rates under water due to varying wavelengths, and considering that atmospheric light is globally constant, thereby influencing underwater image capture, we developed the ALRF module. This caters to the processing of global information within the frequency domain, efficiently negating atmospheric light's impact on underwater images and enhancing their quality and visibility. Our TANet's superior performance is affirmed by extensive experimental results, demonstrating its effectiveness in underwater image enhancement.

Original languageEnglish
Article number122693
JournalExpert Systems with Applications
Volume242
DOIs
StatePublished - 15 May 2024

Keywords

  • Convolutional neural network
  • Gated multipurpose unit
  • Scattering removal
  • Underwater image enhancement

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