Alternating minimization algorithm for hybrid regularized variational image dehazing

Abstract

Imaging quality is often significantly degraded under hazy weather condition. The purpose of this paper is to recover the latent sharp image from its hazy version. It is well known that the accurate estimation of depth information could assist in improving dehazing performance. In this paper, a hybrid regularized variational framework was proposed to simultaneously estimate depth map and haze-free image. In particular, the second-order total generalized variation (TGV) regularizer was introduced to constrain the estimation of depth map, which commonly contains the piecewise smooth regions separated by sharp edges. To take full advantage of the similar properties between three different color channels, we proposed to exploit the multichannel total variation (MTV) regularizer to guarantee the robust restoration of haze-free image. The resulting nonsmooth optimization problem was effectively handled using the alternating direction method of multipliers (ADMM)-based numerical optimization algorithm. A two-step correction mechanism was further introduced to improve visual image quality in the presence of large sky region. Numerous experiments have been implemented based on both synthetic and realistic hazy images. Dehazing results demonstrated that our method was competitive with or even outperformed current state-of-the-art methods under different imaging conditions.