Overview / Usage

Haze and fog reduces the visibility of outdoor scenes. For this reason, distinguishing objects from distance becomes difficult. Haze occurs when light falls on atmospheric particles and gets absorbed and scattered by them. This causes deterioration in the quality, particularly contrast, of the captured image. The strategy for eradicating the effect of haze from such degraded images is known as Image Dehazing. We have solved the image dehazing problem using Fully Convolutional Neural Network and custom designed
loss.

Methodology / Approach

We have designed a Fully Convolutional Neural Network that jointly estimates scene transmittance and airlight. The network is trained using a custom designed loss, called bi-directional consistency loss, that tries to minimize the error to reconstruct the hazy image from clear image and the clear image from hazy image.For more details please read this(http://openaccess.thecvf.com/content_CVPR_2018/papers/w13/Mondal_Image_Dehazing_by_CVPR_2018_paper.pdf) paper.

Technologies Used

Python ,Keras, Tensorflow

Repository

https://github.com/ranjanZ/CVPR2018_Dehazing

Other links

• Image Dehazing API