An illumination-guided dual-domain network for image exposure correction

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI:10.1016/j.jvcir.2024.104313
Jie Yang, Yuantong Zhang, Zhenzhong Chen, Daiqin Yang
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Abstract

Exposure problems, including underexposure and overexposure, can significantly degrade image quality. Poorly exposed images often suffer from coupled illumination degradation and detail degradation, aggravating the difficulty of recovery. These necessitate a spatial discriminating exposure correction, making achieving uniformly exposed and visually consistent images challenging. To address these issues, we propose an Illumination-guided Dual-domain Network (IDNet), which employs a Dual-Domain Module (DDM) to simultaneously recover illumination and details from the frequency and spatial domains, respectively. The DDM also integrates a structural re-parameterization technique to enhance the detail-aware capabilities with reduced computational cost. An Illumination Mask Predictor (IMP) is introduced to guide exposure correction by estimating the optimal illumination mask. The comparison with 26 methods on three benchmark datasets shows that IDNet achieves superior performance with fewer parameters and lower computational complexity. These results confirm the effectiveness and efficiency of our approach in enhancing image quality across various exposure scenarios.
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用于图像曝光校正的照明引导双域网络
曝光问题,包括曝光不足和曝光过度,会显著降低图像质量。曝光不足的图像通常会出现光照衰减和细节衰减,增加了恢复的难度。因此,有必要进行空间判别曝光校正,从而使实现均匀曝光和视觉一致的图像变得具有挑战性。为了解决这些问题,我们提出了一种光照引导双域网络(IDNet),它采用双域模块(DDM)分别从频域和空间域同时恢复光照和细节。DDM 还集成了结构重参数化技术,以增强细节感知能力,同时降低计算成本。此外,还引入了光照掩膜预测器(IMP),通过估计最佳光照掩膜来指导曝光校正。在三个基准数据集上与 26 种方法进行的比较表明,IDNet 以更少的参数和更低的计算复杂度实现了更优越的性能。这些结果证实了我们的方法在各种曝光情况下提高图像质量的有效性和效率。
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来源期刊
Journal of Visual Communication and Image Representation
Journal of Visual Communication and Image Representation 工程技术-计算机:软件工程
CiteScore
5.40
自引率
11.50%
发文量
188
审稿时长
9.9 months
期刊介绍: The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.
期刊最新文献
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