Single image dehazing by latent region-segmentation based transmission estimation and weighted L-1-norm regularisation
Single image dehazing by latent region-segmentation based transmission estimation and weighted L-1-norm regularisation
复制标题
基于潜在区域分割的透射估计和加权 L-1 范数正则化的单图像去雾
DOI：
10.1049/iet-ipr.2016.0377
复制
发表时间：
2017
影响因子：
2.3
通讯作者：
ong
中科院分区：
文献类型：
--
作者：
Cui Tong;Tian Ji;ong;Wang Ende;Tang Y;ong
图像去雾是一种有效的技术，可以消除雾霾天气对图像的影响，提高图像/视频处理算法在雾霾天气下的性能。在这项研究中，提出了一种单一的图像去雾方法。作者基于潜在区域分割适当地估计初始传输，并通过具有新的加权L1范数正则化项的目标函数来细化估计的初始传输。半二次分裂最小化方法被用来解决这个优化问题。他们还定义了一个评估函数来估计可靠的全球大气光。通过细化的透射图和大气光，他们通过雾成像模型恢复了无雾图像。作者的方法与三种最先进的方法进行了比较，并通过两种图像质量评估方法进行了验证。对比实验结果和评价表明，他们的方法可以恢复可比的，甚至更好的结果与清晰的细节，低对比度损失和高对比度在大多数情况下。
Image dehazing is a useful technique which can eliminate the bad effect of haze on images and enhance the performances of image/video processing algorithms in the hazy weather. In this study, a single image dehazing method is proposed. The authors estimate the initial transmission properly based on latent region‐segmentation and refine the estimated initial transmission by an objective function with a novel weightedL1‐norm regularisation term. The half‐quadratic splitting minimisation method is employed to solve this optimisation problem. They also define an evaluation function to estimate the reliable global atmospheric light. With the refined transmission map and atmospheric light they recover the haze‐free image by the haze imaging model. The authors’ method is compared with three state‐of‐the‐art methods and is also validated by two image quality assessment methods. The comparative experimental results and evaluations demonstrate that their method can recover comparable and even better results with clear details, low contrast loss and high contrast in most cases.