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.
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. Many dehazing mechanisms have been developed [
In this paper, we generated the synthetic homogeneous hazes with different concentrations on a clear natural image through atmospheric scattering model [
After generation of haze of different concentrations, we used the dark-channel prior [
The rest of the paper is described as follows: Section 2 explains the haze generation mechanism; Section 3 shows the dehazing mechanism using dark-channel prior; Section 4 presents the experimental results and validation; and finally Section 5 concludes the paper.
In computer graphics, visualization of atmospheric phenomenon is important which has high practical value. A realistic haze will greatly improve the reality of simulated scenes. Special effects in computer games, virtual reality, digital movies, TV, entertainment-industry products and so forth are some applications of the simulated haze. For simulation of a hazy scene, various methods have been developed by using the atmospheric model [
The hazy image formation model can be described by using the following equation [
I ( x ) = J ( x ) t ( x ) + A ( 1 − t ( x ) ) (1)
x = (x, y) is a 2D vector that represents the coordinates of a pixel’s location in the image. I is the input hazy image, J is the scene radiance, t is the medium transmission, A is the global atmospheric light.
In the above Equation (1), the term in the first part of right-side J ( x ) t ( x ) is called direct attenuation and the term in second part of right-side A ( 1 − t ( x ) ) is called airtight.
Here, we simulate haze using above atmospheric scattering model with and without Perline noise. The flow steps to simulate haze from atmospheric scattering model on an input clear image are shown in
Here, at first, we calculate the depth map of an image. For scene depth restoration, a linear model given in Equation (2) is used. The concentration (density) of haze increases along with the decreases of scene depth. Density of haze is the disparity between the brightness and the saturation. Then it can create a linear model.
We can express this linear model as:
d ( x ) = θ 0 + θ 1 v ( x ) + θ 2 s ( x ) + ε ( x ) (2)
where x is the position within the image, d is the scene depth, v is the brightness component of the hazy image, s is the saturation component, θ 0 , θ 1 , θ 2 are the unknown linear coefficients, ε(x) is a random variable represents the random error of the model that is regarded as a random image. A simple and efficient supervised learning method is used to determine the coefficients θ 0 , θ 1 , θ 2 . The training data [
Raw depth map is determined based on a hypothesis that the scene depth is locally constant as
d r ( x ) = min y ∈ Ω r ( x ) d ( y ) (3)
where Ω r ( x ) is an r × r neighborhood centered at x, and d r is the depth map with scale r. However, it is also obvious that the blocking artifacts may present in the image. To overcome these artifacts a bilateral filter is used that generates a refine transmission map [
Since we already have the clear image J(x), the refined transmission map, and the air light (which can be set as 255), we can easily simulate the hazy scene according to Equation (1).
We can also generate hazy scenes with different haze densities assuming the transmission medium:
t ( x ) = e − β d ( x ) λ ,
where β is a coefficient and λ is the haze density factor λ.
However, haze is not always perfectly homogeneous in real situations. Therefore, Perlin noise, which is a gradient noise is introduced in our method though the following Equation (4)
R ( x ) = I ( x ) + k ∗ n ( x ) (4)
Here, I is the hazy image that is obtained by using our haze simulation technique, k is used to control the appearance of Perlin’s turbulence texture and n is the perline noise image. Amplitude and frequency are the two properties that characterize the Perline noise function [
Haze effect minimization in real scene is very important and it finds wide applications. Previously, we modified a single image haze removal algorithm [
For removal of haze we used dark channel prior algorithm. This algorithm mainly used to estimate patch size for direct attenuation and guided filtering. Haze removal model can be shown as:
J ( x ) = I ( x ) − A max ( t ( x ) , t 0 ) + A (5)
The flow diagram of haze removal technique based on dark-channel prior is shown in
The visual (subjective) representations of dehazing through dark-channel prior
method for homogeneous and heterogeneous hazy situations with three different haze concentrations are shown in
A G = G x 2 + G y 2 (6)
Here, AG is average gradient, Gx and Gy are the horizontal and vertical gradients, respectively. The objective evaluation results of
Image Dehazing for Homogeneous Hazing Condition | ||
---|---|---|
Haze Concentration | Average Gradient | |
Low Haze | 12.33 | |
Medium Haze | 9.73 | |
High Haze | 9.21 |
Image Dehazing for Heterogeneous Hazing Condition (Using Perlin Noise) | ||
---|---|---|
Haze Concentration | Average Gradient | |
Low Haze | 11.09 | |
Medium Haze | 8.31 | |
High Haze | 8.81 |
In this work, synthetic hazes are generated on a real scene through atmospheric model with and without Perlin noise. After that we successfully performed the validation of a prominent single image dehazing technique―the dark-channel prior through subjective and objective measures. Here, a well-established dehazing objective metric average gradient is used. The simulated haze will find application in validating any new dehazing technique. In addition, it will be used for many outdoor visual enhancements such as surveillance and navigation systems, real time tasks processing by robots etc.
The authors declare no conflicts of interest regarding the publication of this paper.
Sarker, A., Akter, M. and Uddin, M.S. (2019) Simulation of Hazy Image and Validation of Haze Removal Technique. Journal of Computer and Communications, 7, 62-72. https://doi.org/10.4236/jcc.2019.72005