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

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.