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

TLDR

Single image haze removal is a challenging ill‑posed problem that relies on estimating a medium transmission map, and existing methods use various constraints and priors to achieve plausible solutions. We propose DehazeNet, an end‑to‑end trainable system that estimates the medium transmission map and introduces a Bilateral Rectified Linear Unit to improve the quality of the recovered haze‑free image. DehazeNet is a CNN‑based architecture that maps a hazy image to its medium transmission map, then recovers a haze‑free image via the atmospheric scattering model, employing Maxout units for feature extraction and a Bilateral Rectified Linear Unit activation. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods while remaining efficient and easy to use.

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.