A new two-stage low-light enhancement network with progressive attention fusion strategy

IF 3.4 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Hegui Zhu , Luyang Wang , Zhan Gao , Yuelin Liu , Qian Zhao
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引用次数: 0

Abstract

Low-light image enhancement is a very challenging subject in the field of computer vision such as visual surveillance, driving behavior analysis, and medical imaging . It has a large number of degradation problems such as accumulated noise, artifacts, and color distortion. Therefore, how to solve the degradation problems and obtain clear images with high visual quality has become an important issue. It can effectively improve the performance of high-level computer vision tasks. In this study, we propose a new two-stage low-light enhancement network with a progressive attention fusion strategy, and the two hallmarks of this method are the use of global feature fusion (GFF) and local detail restoration (LDR), which can enrich the global content of the image and restore local details. Experimental results on the LOL dataset show that the proposed model can achieve good enhancement effects. Moreover, on the benchmark dataset without reference images, the proposed model also obtains a better NIQE score, which outperforms most existing state-of-the-art methods in both quantitative and qualitative evaluations. All these verify the effectiveness and superiority of the proposed method.
采用渐进式注意力融合策略的新型两级低照度增强网络
低照度图像增强是视觉监控、驾驶行为分析和医学成像等计算机视觉领域一个极具挑战性的课题。它存在大量的劣化问题,如累积噪声、伪像和色彩失真。因此,如何解决退化问题,获得视觉质量高的清晰图像已成为一个重要问题。它可以有效提高高级计算机视觉任务的性能。在本研究中,我们提出了一种新的两阶段低照度增强网络,采用渐进式注意力融合策略,该方法的两大特点是使用全局特征融合(GFF)和局部细节还原(LDR),既能丰富图像的全局内容,又能还原局部细节。在 LOL 数据集上的实验结果表明,所提出的模型可以达到良好的增强效果。此外,在没有参考图像的基准数据集上,所提出的模型也获得了较好的 NIQE 分数,在定量和定性评估中均优于大多数现有的先进方法。所有这些都验证了所提出方法的有效性和优越性。
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来源期刊
Signal Processing-Image Communication
Signal Processing-Image Communication 工程技术-工程:电子与电气
CiteScore
8.40
自引率
2.90%
发文量
138
审稿时长
5.2 months
期刊介绍: Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following: To present a forum for the advancement of theory and practice of image communication. To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems. To contribute to a rapid information exchange between the industrial and academic environments. The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world. Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments. Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.
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