Density-Aware Diffusion Model for Efficient Image Dehazing

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dc.contributor.authorZhang, Lingen_US
dc.contributor.authorBai, Wenxuen_US
dc.contributor.authorXiao, Chunxiaen_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:08:06Z
dc.date.available2024-10-13T18:08:06Z
dc.date.issued2024
dc.description.abstractExisting image dehazing methods have made remarkable progress. However, they generally perform poorly on images with dense haze, and often suffer from unsatisfactory results with detail degradation or color distortion. In this paper, we propose a density-aware diffusion model (DADM) for image dehazing. Guided by the haze density, our DADM can handle images with dense haze and complex environments. Specifically, we introduce a density-aware dehazing network (DADNet) in the reverse diffusion process, which can help DADM gradually recover a clear haze-free image from a haze image. To improve the performance of the network, we design a cross-feature density extraction module (CDEModule) to extract the haze density for the image and a density-guided feature fusion block (DFFBlock) to learn the effective contextual features. Furthermore, we introduce an indirect sampling strategy in the test sampling process, which not only suppresses the accumulation of errors but also ensures the stability of the results. Extensive experiments on popular benchmarks validate the superior performance of the proposed method. The code is released in https://github.com/benchacha/DADM.en_US
dc.description.number7
dc.description.sectionheadersImage and Video Enhancement II
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15221
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15221
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15221
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → Image dehazing; Density-aware; Diffusion model
dc.subjectComputing methodologies → Image dehazing
dc.subjectDensity
dc.subjectaware
dc.subjectDiffusion model
dc.titleDensity-Aware Diffusion Model for Efficient Image Dehazingen_US
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