Image Haze Removal Based on Superpixels and Markov Random Field
Image haze removal is critical for autonomous driving. However, it is a challenging task for the existing image dehazing algorithms to eliminate the block effect completely and handle objects similar to light (such as snowy objects and white buildings). To address this problem, we propose a novel si...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a09ce9411daa49a5ac491306854abaca |
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| Sumario: | Image haze removal is critical for autonomous driving. However, it is a challenging task for the existing image dehazing algorithms to eliminate the block effect completely and handle objects similar to light (such as snowy objects and white buildings). To address this problem, we propose a novel single-image dehazing method based on superpixels and Markov random field. We obtain the transmission map in the superpixel domain to eliminate the block/halo effect and introduce Markov random field to revise the transmission map in the superpixel domain. The key idea is that the sparsely distributed, incorrectly estimated transmittances can be corrected by properly characterizing the spatial dependencies between the incorrectly estimated superpixels and the neighbouring well-estimated superpixels. The experimental results demonstrate that the proposed method outperforms state-of-the-art image dehazing methods. |
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