Alternating minimization algorithm for hybrid regularized variational image dehazing
Alternating minimization algorithm for hybrid regularized variational image dehazing
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DOI：
10.1016/j.ijleo.2019.04.002
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发表时间：
2019-05
期刊：
影响因子：
3.1
通讯作者：
Q. Shu;Chuansheng Wu;Qiuxiang Zhong;R. W. Liu
中科院分区：
文献类型：
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作者：
Q. Shu;Chuansheng Wu;Qiuxiang Zhong;R. W. Liu
在有雾天气条件下，成像质量往往会显著下降。本文的目的是从模糊的图像中恢复潜在的清晰图像。众所周知，深度信息的准确估计可以帮助改善去雾性能。本文提出了一种混合正则化变分框架来同时估计深度图和无雾图像。特别地，二阶总广义变分（TGV）正则化被引入到约束深度图的估计，其中通常包含由尖锐边缘分离的分段光滑区域。为了充分利用三个不同颜色通道之间的相似性，我们提出了利用多通道总变差（MTV）正则化，以保证鲁棒的无雾图像恢复。基于交替方向乘子法的数值优化算法有效地处理了非光滑优化问题。在大空域的情况下，进一步引入了两步校正机制来改善视觉图像质量。大量的实验已经实现了基于合成和现实朦胧图像。去雾结果表明，我们的方法是有竞争力的，甚至优于当前国家的最先进的方法在不同的成像条件下。
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.