DehazeNet: An End-to-End System for Single Image Haze Removal

TL;DR

DehazeNet is an end-to-end deep learning system that estimates medium transmission maps from hazy images to effectively remove haze, outperforming existing methods in accuracy and efficiency.

Contribution

The paper introduces DehazeNet, a novel CNN-based architecture with a new activation function, BReLU, specifically designed for single image haze removal.

Findings

DehazeNet outperforms existing haze removal methods on benchmark datasets.

The use of BReLU improves the quality of haze-free images.

DehazeNet is efficient and easy to deploy in practical applications.

Abstract

Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. DehazeNet adopts Convolutional Neural Networks (CNN) based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing. Specifically, layers of Maxout units are used for feature extraction, which can generate almost all haze-relevant features. We also propose a novel nonlinear activation function in DehazeNet, called Bilateral Rectified Linear Unit (BReLU), which is able to improve the quality of recovered haze-free image. We establish connections between components of the proposed DehazeNet and those used in existing methods. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods, yet keeps efficient and easy to use.