Published May 1, 2021 | Version Published
Journal article Open

A Framework for Objective Evaluation of Single Image De-Hazing Techniques

  • 1. DeepCamera MRG Laboratory, CYENS–Centre of Excellence, Nicosia, Cyprus
  • 2. DeepCamera MRG Laboratory, CYENS–Centre of Excellence, Nicosia, Cyprus Hellenic Military Academy, Attica, Greece University of West Attica, Attica, Greece


Real-world environment, where images are acquired with digital camera, may be subject to
sever climatic conditions such as haze that may drastically reduce the quality performance of sophisticated
computer vision algorithms used for various tasks, e.g., tracking, detection, classication etc. Even though
several single image de-hazing techniques have been recently proposed with many deep-learning approaches
among them, a general statistical framework that would permit an objective performance evaluation has not
been independently introduced yet. In this manuscript, certain performance metrics that emphasize different
aspects of image quality, output ranges and polarity, are identied and combined into a single performance
indicator derived in an unbiased manner. A general methodology is thus introduced, as a framework for
objective performance evaluation of current and future dehazing tasks, through an extensive comparison
of 15 single image de-hazing techniques over a vast range of image data sets. The proposed unied
framework shows several advantages in evaluating diverse and perceptually meaningful image features but
also in elucidating future directions for improvement in image dehazing tasks.


This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.


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RISE – Research Center on Interactive Media, Smart System and Emerging Technologies 739578
European Commission