用于图像去重的自适应多特征注意网络

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hongyuan Jing, Jiaxing Chen, Chenyang Zhang, Shuang Wei, Aidong Chen, Mengmeng Zhang
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引用次数: 0

摘要

目前,基于深度学习的图像去毛刺方法在图像去毛刺应用中占据主导地位。尽管许多复杂的去毛刺模型已经取得了具有竞争力的去毛刺性能,但提取有用特征的有效方法仍未得到充分研究。因此,本文提出了一种由点加权注意力(PWA)机制和多层特征融合(AMLFF)组成的自适应多特征注意力网络(AMFAN)。我们首先增强了每个特征图的像素级注意力。具体来说,我们设计了一个 PWA 块,用于聚合特征图的全局和局部信息。我们还利用 PWA 使模型自适应地关注重要通道/区域。然后,我们设计了一个特征融合块(FFB),通过利用 PWA 块来完成特征级融合。FFB 和 PWA 构成了我们的 AMLFF。我们设计的 AMLFF 可以整合三个不同层次的特征图,从而有效平衡编码器和解码器的输入权重。我们还利用对比损失函数来训练去噪网络,使恢复的图像远离负样本,接近正样本。在合成图像和真实世界图像上的实验结果表明,这种去毛刺方法在视觉上和定量上都超越了许多其他先进技术,展示了其在图像去毛刺方面的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Multi-Feature Attention Network for Image Dehazing
Currently, deep-learning-based image dehazing methods occupy a dominant position in image dehazing applications. Although many complicated dehazing models have achieved competitive dehazing performance, effective methods for extracting useful features are still under-researched. Thus, an adaptive multi-feature attention network (AMFAN) consisting of the point-weighted attention (PWA) mechanism and the multi-layer feature fusion (AMLFF) is presented in this paper. We start by enhancing pixel-level attention for each feature map. Specifically, we design a PWA block, which aggregates global and local information of the feature map. We also employ PWA to make the model adaptively focus on significant channels/regions. Then, we design a feature fusion block (FFB), which can accomplish feature-level fusion by exploiting a PWA block. The FFB and PWA constitute our AMLFF. We design an AMLFF, which can integrate three different levels of feature maps to effectively balance the weights of the inputs to the encoder and decoder. We also utilize the contrastive loss function to train the dehazing network so that the recovered image is far from the negative sample and close to the positive sample. Experimental results on both synthetic and real-world images demonstrate that this dehazing approach surpasses numerous other advanced techniques, both visually and quantitatively, showcasing its superiority in image dehazing.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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