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Improved single image dehazing methods for resource-constrained platforms
Journal of Real-Time Image Processing ( IF 3 ) Pub Date : 2021-06-12 , DOI: 10.1007/s11554-021-01143-6
Gengqian Yang , Adrian N. Evans

Image dehazing is an increasingly widespread approach to address the degradation of images of the natural environment by low-visibility weather, dust and other phenomena. Advances in autonomous systems and platforms have increased the need for low-complexity, high-performing dehazing techniques. However, while recent learning-based image dehazing approaches have significantly increased the dehazing performance, this has often been at the expense of complexity and hence the use of prior-based approaches persists, despite their lower performance. This paper addresses both these aspects and focuses on single image dehazing, the most practical class of techniques. A new Dark Channel Prior-based single image dehazing algorithm is presented that has an improved atmospheric light estimation method and a low-complexity morphological reconstruction. In addition, a novel, lightweight end-to-end network is proposed, that avoids information loss and significant computational effort by eliminating the pooling and fully connected layers. Qualitative and quantitative evaluations show that our proposed algorithms are competitive with, or outperform, state-of-the-art techniques with significantly lower complexity, demonstrating their suitability for use in resource-constrained platforms.



中文翻译:

改进的资源受限平台的单图像去雾方法

图像去雾是一种越来越普遍的方法,用于解决低能见度天气、灰尘和其他现象造成的自然环境图像质量下降问题。自主系统和平台的进步增加了对低复杂性、高性能去雾技术的需求。然而,虽然最近基于学习的图像去雾方法显着提高了去雾性能,但这通常是以复杂性为代价的,因此尽管性能较低,但仍继续使用基于先验的方法。本文讨论了这两个方面,并侧重于单幅图像去雾,这是最实用的一类技术。提出了一种新的基于暗通道先验的单幅图像去雾算法,该算法具有改进的大气光估计方法和低复杂度的形态重建。此外,还提出了一种新颖的轻量级端到端网络,通过消除池化层和全连接层来避免信息丢失和大量计算工作。定性和定量评估表明,我们提出的算法与具有显着较低复杂性的最先进技术竞争或优于最先进的技术,证明它们适用于资源受限平台。

更新日期:2021-06-13
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