Frequency-based haze and rain removal network (Fhrr-net) with deep convolutional encoder-decoder

Dong Hwan Kim, Woo Jin Ahn, Myo Taeg Lim, Tae Koo Kang, Dong Won Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Removing haze or rain is one of the difficult problems in computer vision applications. On real-world road images, haze and rain often occur together, but traditional methods cannot solve this imaging problem. To address rain and haze problems simultaneously, we present a robust network-based framework consisting of three steps: Image decomposition using guided filters, a frequency-based haze and rain removal network (FHRR-Net), and image restoration based on an atmospheric scattering model using predicted transmission maps and predicted rain-removed images. We demonstrate FHRR-Nets capabilities with synthesized and real-world road images. Experimental results show that our trained framework has superior performance on synthesized and real-world road test images compared with state-of-the-art methods. We use PSNR (peak signal-to-noise) and SSIM (structural similarity index) indicators to evaluate our model quantitatively, showing that our methods have the highest PSNR and SSIM values. Furthermore, we demonstrate through experiments that our method is useful in real-world vision applications.

Original languageEnglish
Article number2873
JournalApplied Sciences (Switzerland)
Volume11
Issue number6
DOIs
Publication statusPublished - 2021 Mar 2

Keywords

  • Dehaze
  • Derain
  • Dilated convolution
  • Encoder-decoder network
  • Guided filter
  • Image restoration

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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