# Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination

## TL;DR

This paper introduces an end-to-end neural network system for image dehazing that jointly estimates transmittance and environmental illumination, improving haze removal accuracy by leveraging their relationship.

## Contribution

It presents a novel multi-scale CNN that simultaneously estimates transmittance and illumination, addressing the often overlooked impact of environmental lighting on dehazing quality.

## Key findings

- Accurate estimation of transmittance and illumination improves dehazing results.
- The method outperforms existing approaches in qualitative and quantitative evaluations.
- Joint estimation enhances the natural appearance of dehazed images.

## Abstract

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 a hazy image and its corresponding transmittance map 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.