Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination

Sanchayan Santra, Ranjan Mondal, Pranoy Panda, Nishant Mohanty, Shubham Bhuyan

Haze limits the visibility of outdoor images, due to the existence of fog, smoke and dust in the atmosphere. Image dehazing methods try to recover haze-free image by removing the effect of haze from a given input image. In this paper, we present an end to end system, which takes a hazy image as its input and returns a dehazed image. The proposed method learns the mapping between hazy images and their corresponding transmittance maps and the environmental illumination, by using a multi-scale Convolutional Neural Network. Although most of the time haze appears grayish in color, its color may vary depending on the color of the environmental illumination. Very few of the existing image dehazing methods have laid stress on its accurate estimation. But the color of the dehazed image and the estimated transmittance depends on the environmental illumination. Our proposed method exploits the relationship between the transmittance values and the environmental illumination as per the haze imaging model and estimates both of them. Qualitative and quantitative evaluations show, the estimates are accurate enough.

Published in [ICAPR 2017]
Preprint paper: [pdf]

Results: More results