Density-Aware Diffusion Model for Efficient Image Dehazing

IF 2.7 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Ling Zhang, Wenxu Bai, Chunxia Xiao
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引用次数: 0

Abstract

Existing 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.

用于高效图像去重的密度感知扩散模型
现有的图像去毛刺方法已经取得了显著的进步。然而,这些方法在处理雾度较高的图像时通常表现不佳,而且经常出现细节退化或色彩失真等令人不满意的结果。在本文中,我们提出了一种用于图像去毛刺的密度感知扩散模型(DADM)。在雾霾密度的指导下,我们的 DADM 可以处理雾霾密集和环境复杂的图像。具体来说,我们在反向扩散过程中引入了密度感知去雾网络(DADNet),它可以帮助 DADM 从雾霾图像中逐步恢复出清晰的无雾霾图像。为了提高该网络的性能,我们设计了一个交叉特征密度提取模块(CDEModule)来提取图像的雾霾密度,并设计了一个密度引导特征融合模块(DFFBlock)来学习有效的上下文特征。此外,我们还在测试采样过程中引入了间接采样策略,这不仅抑制了误差的积累,还确保了结果的稳定性。在流行基准上进行的大量实验验证了所提方法的优越性能。代码发布于 https://github.com/benchacha/DADM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computer Graphics Forum
Computer Graphics Forum 工程技术-计算机:软件工程
CiteScore
5.80
自引率
12.00%
发文量
175
审稿时长
3-6 weeks
期刊介绍: Computer Graphics Forum is the official journal of Eurographics, published in cooperation with Wiley-Blackwell, and is a unique, international source of information for computer graphics professionals interested in graphics developments worldwide. It is now one of the leading journals for researchers, developers and users of computer graphics in both commercial and academic environments. The journal reports on the latest developments in the field throughout the world and covers all aspects of the theory, practice and application of computer graphics.
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