Transmission map estimation of weather-degraded images using a hybrid of recurrent fuzzy cerebellar model articulation controller and weighted strategy

Jyun Guo Wang, Shen Chuan Tai, Cheng Jian Lin

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

This study proposes a hybrid of a recurrent fuzzy cerebellar model articulation controller (RFCMAC) and a weighted strategy for solving single-image visibility in a degraded image. The proposed RFCMAC model is used to estimate the transmission map. The average value of the brightest 1% in a hazy image is calculated for atmospheric light estimation. A new adaptive weighted estimation is then used to refine the transmission map and remove the halo artifact from the sharp edges. Experimental results show that the proposed method has better dehazing capability compared to state-of-the-art techniques and is suitable for real-world applications.

Original languageEnglish
Article number083104
JournalOptical Engineering
Volume55
Issue number8
DOIs
Publication statusPublished - 2016 Aug 1

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)

Fingerprint Dive into the research topics of 'Transmission map estimation of weather-degraded images using a hybrid of recurrent fuzzy cerebellar model articulation controller and weighted strategy'. Together they form a unique fingerprint.

Cite this