A Mixed Transmission Estimation Iterative Method for Single Image Dehazing

Abstract

Considering the frequent occurrence of hazy weather, single image dehazing has become an important research task. Physical model-based and deep learning-based methods are two competitive methods in single image dehazing, but achieving fidelity and effectively removing haze at the same time in real hazy scenes is still a challenging problem. In this work, we propose a mixed iterative model to restore high-quality clear images by integrating a physical model-based approach and a learning-based approach, which can combine the advantages of the physical model-based method in maintaining natural attributes and the advantages of the learning-based method in completely removing haze. For a hazy image, first, according to the haze density, it is divided into separate regions to calculate the local atmospheric light. Then, we utilize the dark channel prior and DehazeNet to jointly estimate the transmission, which can estimate an accurate recovered image that is more in line with the real scene. Finally, a numerical iterative strategy is employed to further optimize the atmospheric light and transmission. The experiments on both synthetic and natural hazy image datasets indicate that the proposed method can restore natural and clean images and has superior performance than other state-of-the-art algorithms in peak signal-to-noise ratio, structural similarity, information entropy, color natural index, and color histogram. And in the four indicators of the quantitative results of the synthetic hazy test set, we achieve the highest score in three of them and the second highest result in another. In the comparison of the two quantitative indicators of natural hazy images, our average objective results are both the highest.INDEX TERMS Image dehazing, numerical iterative, mixed model, joint transmission.

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

“…Notably, parameters estimated via deep learning exhibit a higher degree of accuracy compared to their non-deep learning physical model counterparts, resulting in a more pronounced improvement in de-fogging effectiveness [54][55][56][57][58]. Parameter estimation through deep learning methods allows for greater flexibility in adapting to a variety of complex scenarios and data distributions, resulting in higher levels of performance improvement [59][60][61][62][63].…”

Research progress and applications of image defogging algorithms

“…The algorithm can handle haze images with different concentrations, and can effectively restore image details and enhance visual outcomes. Yang et al proposed a hybrid iterative model that combines the dark channel prior theory and DehazeNet algorithms to better restore haze-free images that are closer to real scenes, but the algorithm is computationally complex [45]. Non-end-to-end defogging algorithms have the support of physical theory, which makes the dehazed images closer to real scenes, but they have higher parameter count and lower algorithmic simplicity.…”