Atmospheric Haze

Haze is an atmospheric phenomenon that arises when dust, smoke and other atmospheric particles absorb and scatter light.

The radiance of objects attenuates as it traverses from the scene point to the camera and blends with additive atmospheric light, corrupting the visibility of the scene.

In computer vision, a hazy image, I(x) is determined by the amount of transmitted light that remains from the the clear scene, J(x), after travelling a distance, d(x), and the atmospheric light intensity, A(x).

I(x) = J(x) + J(x)t(x) + A(x)(1 – t(x))

The amount of particulates in the atmosphere cause the transmitted light to decay exponentially with the distance travelled. It is represented by the extinction coefficient, β.

t(x) = exp(-βd(x))

Single Image Dehazing

An attempt to recover the clear image by solving the atmospheric light scattering model.

Here at DeepCamera we dedicate our research for understanding, analysing and improving the task of single image dehazing. We focus on building the foundations to foster algorithmic innovation in dehazing for both the classical image processing approach and the modern deep learning domain.

Our team has created its very own repository of hazy datasets. Using the atmospheric scattering model, we were able to render 45 different levels of atmospheric haze on the Microsoft Common Objects in Context (COCO) dataset. Our collection holds over 150K fully annotated images, ready to benchmark tasks such as classification and object detection under different hazy conditions.

The single image dehazing literature has accumulated a wealth of algorithms over the years, and with them several evaluation metrics have spawned. We realise that while these evaluation metrics are successful in their intended purpose, they each address different aspects of the task. Our team has eliminated the need to trade off in evaluation by formulating a single objective performance indicator out the combination of these metrics.