Abstract
Haze hampers the performance of vision systems. So, removal of haze appearance in a scene should be the first-priority for clear vision. It finds wide spectrum of practical applications. A good number of dehazing techniques have already been developed. However, validation with the help of ground truth i.e. simulated haze on a clear image is an ultimate necessity. To address this issue, in this work synthetic haze images with various haze concentrations are simulated and then used to confirm the validation task of dark-channel dehazing mechanism, as it is a very promising single image dehazing technique. The simulated hazy image is developed using atmospheric model with and without Perlin noise. The effectiveness of dark-channel dehazing method is confirmed using the simulated haze images through average gradient metric, as haze reduces the gradient score.
Highlights
Haze is a natural phenomenon that causes obstruction to vision
The visual representations of dehazing through dark-channel prior method for homogeneous and heterogeneous hazy situations with three different haze concentrations are shown in Figure 7 and Figure 8, respectively
In addition to subjective measure, the validation is performed using a well-known dehazing objective metric—the average gradient shown in Equation (6)
Summary
Haze is a natural phenomenon that causes obstruction to vision. For clear vision, dehazing is an ultimate necessity that finds diverse applications such as navigation of vehicles, outdoor movements of people, surveillance system and so on. Methods mentioned in references [2] [3] use a pair or multiple images of the same scene for haze removal through polarizing filter. This polarized-filter is not effective in situation where changes in images are more rapid than the rotation of filter [4]. Method shown in reference [5] estimates the complete 3D structure and recovers haze free image from two or more bad weather images. Some of these methods give good results, but, have limited
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