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Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems
Journal of Advanced Transportation ( IF 2.3 ) Pub Date : 2021-05-03 , DOI: 10.1155/2021/6658763
Xianjun Hu 1 , Jing Wang 2 , Chunlei Zhang 1 , Yishuo Tong 1
Affiliation  

Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version. It is well known that the accurate estimation of transmission map plays a vital role in image dehazing. In this work, the coarse transmission map is firstly estimated using a robust fusion-based strategy. A unified optimization framework is then proposed to estimate the refined transmission map and latent sharp image simultaneously. The resulting constrained minimization model is solved using a two-step optimization algorithm. To further enhance dehazing performance, the solutions of subproblems obtained in this optimization algorithm are equivalent to deep learning-based image denoising. Due to the powerful representation ability, the proposed method can accurately and robustly estimate the transmission map and latent sharp image. Numerous experiments on both synthetic and realistic datasets have been performed to compare our method with several state-of-the-art dehazing methods. Dehazing results have demonstrated the proposed method’s superior imaging performance in terms of both quantitative and qualitative evaluations. The enhanced imaging quality is beneficial for practical applications in maritime ITS, for example, vessel detection, recognition, and tracking.

中文翻译:

海上智能运输系统中基于深度学习的变分优化图像去雾方法

图像去雾已成为计算机视觉驱动的海上智能运输系统(ITS)中普遍关注的基本问题。图像去雾的目的是从其观察到的朦胧版本中重建无潜影的图像。众所周知,透射图的准确估计在图像去雾中起着至关重要的作用。在这项工作中,首先使用鲁棒的基于融合的策略来估计粗略的传输图。然后提出了一个统一的优化框架来同时估计精化的透射图和潜在的锐利图像。使用两步优化算法求解得到的约束最小化模型。为了进一步提高除雾性能,在此优化算法中获得的子问题的解决方案等效于基于深度学习的图像去噪。由于其强大的表示能力,该方法可以准确,鲁棒地估计透射图和潜在的清晰图像。已对合成数据集和实际数据集进行了大量实验,以将我们的方法与几种最新的除雾方法进行比较。除雾结果已证明该方法在定量和定性评估方面均具有出色的成像性能。增强的成像质量有利于海上ITS的实际应用,例如船只检测,识别和跟踪。已对合成数据集和实际数据集进行了大量实验,以将我们的方法与几种最新的除雾方法进行比较。除雾结果已证明该方法在定量和定性评估方面均具有出色的成像性能。增强的成像质量有利于海上ITS的实际应用,例如船只检测,识别和跟踪。已对合成数据集和实际数据集进行了大量实验,以将我们的方法与几种最新的除雾方法进行比较。除雾结果已证明该方法在定量和定性评估方面均具有出色的成像性能。增强的成像质量有利于海上ITS的实际应用,例如船只检测,识别和跟踪。
更新日期:2021-05-03
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