Single image dehazing via decomposition and enhancement,IET Image Processing

当前位置： X-MOL 学术 › IET Image Process. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Single image dehazing via decomposition and enhancement
IET Image Processing ( IF 2.0 ) Pub Date : 2023-11-27 , DOI: 10.1049/ipr2.13003
Bo Gu ₁ , Haohan Yao ₁ , Yanjun Sun ₁ , Zhonghang Duan ₁

Affiliation

Hazy images suffer from two problems. The low contrast can be enhanced by estimating a transmission layer, and the colour cast can be restored by estimating an airlight. These two variables, together with the albedo layer, are the constitutive elements of a hazy image. The resulting quality of dehazed images is inextricably linked to the accurate estimation of these components. However, it is ill-posed to decompose these variables from a single image. As such, this paper presents an innovative algorithm intended to facilitate the optimal decomposition of a hazy image. Using the Markov random field model, an optimal framework is established that allows the simultaneous estimation of the three components across the three-colour channels. To improve the visual quality, three improvements are proposed in the variational solution for the optimal components. The dehazed result is recomposed from the components with the transmission enhanced to circumvent any potential artefacts or information loss. Extensive experiments on natural images corroborate that the proposed algorithm outperforms state-of-the-art dehazing methods, both qualitatively and quantitatively.

中文翻译：

通过分解和增强对单幅图像进行去雾

模糊图像有两个问题。可以通过估计透射层来增强低对比度，并且可以通过估计空气光来恢复色偏。这两个变量与反照率层一起是模糊图像的构成元素。去雾图像的最终质量与这些成分的准确估计密不可分。然而，从单个图像中分解这些变量是不合适的。因此，本文提出了一种创新算法，旨在促进模糊图像的最佳分解。使用马尔可夫随机场模型，建立了一个最佳框架，允许同时估计三色通道上的三个分量。为了提高视觉质量，在最优组件的变分解中提出了三项改进。去雾结果由增强传输的组件重新组合，以避免任何潜在的伪影或信息丢失。对自然图像的大量实验证实，所提出的算法在定性和定量上都优于最先进的去雾方法。

更新日期：2023-11-27

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南