Liu Qi, Gao Xinbo, He Lihuo, Lu Wen
IEEE Trans Image Process. 2018 Jun 25. doi: 10.1109/TIP.2018.2849928.
Single image dehazing can benefit many computer vision applications hence has attracted much more attention in recent years. However, it still remains a challenging task due to its double uncertainty of scene transmission and scene radiance. The existing image dehazing methods usually impair edges in the estimated transmission which leads to halo effects in the dehazing results. Besides, most existing methods suffer from noise and artifacts amplification in dense haze region after dehazing. To address these challenges, we propose a transmission adaptive regularized image recovery method for high quality single image dehazing. An initial transmission map is first obtained by a boundary constraint on the haze model. Then it is refined by applying a non-local total variation (NLTV) regularization to keep depth structures while smoothing excessive details. Noticing that the artifacts amplification effect depends on scene transmission, a transmission adaptive regularized recovery method based on NLTV is proposed to simultaneously suppress visual artifacts and preserve image details in the final dehazing result. An efficient alternating optimization algorithm is also proposed to solve the regularization model. Thorough experimental results demonstrate that the proposed method can effectively suppress visual artifacts for degraded hazy images, and yields high-quality results comparative to the state-of-the-art dehazing methods both quantitatively and qualitatively.
单图像去雾可使许多计算机视觉应用受益，因此近年来受到了更多关注。然而，由于其场景透射率和场景辐射率的双重不确定性，它仍然是一项具有挑战性的任务。现有的图像去雾方法通常会在估计的透射率中削弱边缘，这会导致去雾结果中出现光晕效应。此外，大多数现有方法在去雾后的浓雾区域会出现噪声和伪影放大的问题。为了解决这些挑战，我们提出了一种用于高质量单图像去雾的透射自适应正则化图像恢复方法。首先通过对雾模型施加边界约束来获得初始透射率图。然后通过应用非局部总变差（NLTV）正则化对其进行细化，以保留深度结构，同时平滑过多的细节。注意到伪影放大效应取决于场景透射率，提出了一种基于NLTV的透射自适应正则化恢复方法，以在最终的去雾结果中同时抑制视觉伪影并保留图像细节。还提出了一种有效的交替优化算法来求解正则化模型。全面的实验结果表明，所提出的方法可以有效地抑制退化模糊图像的视觉伪影，并且在定量和定性方面都能产生与当前最先进的去雾方法相比高质量的结果。
