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首页> 外文期刊>Computer vision and image understanding >Accurate MR image super-resolution via lightweight lateral inhibition network
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Accurate MR image super-resolution via lightweight lateral inhibition network

机译：通过轻量级横向抑制网络精确图像超分辨率

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摘要

In recent years, convolutional neural networks (CNNs) have shown their advantages on MR image super-resolution (SR) tasks.Many current SR models, however, have heavy demands on computation and memory, which are not friendly to magnetic resonance imaging (MRI) where computing resource is usually constrained.On the other hand, a basic consideration in most MRI experiments is how to reduce scanning time to improve patient comfort and reduce motion artifacts.In this work, we ease the problem by presenting an effective and lightweight model that supports fast training and accurate SR inference.The proposed network is inspired by the lateral inhibition mechanism, which assumes that there exist inhibitory effects between adjacent neurons.The backbone of our network consists of several lateral inhibition blocks, where the inhibitory effect is explicitly implemented by a battery of cascaded local inhibition units.When model scale is small, explicitly inhibiting feature activations is expected to further explore model representational capacity.For more effective feature extraction, several parallel dilated convolutions are also used to extract shallow features directly from the input image.Extensive experiments on typical MR images demonstrate that our lateral inhibition network (LIN) achieves better SR performance than other lightweight models with similar model scale.
机译：近年来，卷积神经网络（CNNS）已经对MR图像超分辨率（SR）任务的优点显示了它们对计算和存储器的大需求，这对磁共振成像不友好（MRI另一方面，在计算资源通常被约束。另一方面，大多数MRI实验中的基本考虑是如何减少扫描时间以提高患者的舒适度，减少运动伪影。在这项工作中，我们通过呈现有效和轻量级的模型来缓解问题支持快速训练和准确的SR推断。建议的网络受到横向抑制机制的启发，这假设相邻神经元之间存在抑制作用。我们网络的骨干包括几个横向抑制块，其中明确实施抑制效果通过级联局部抑制单位的电池。模型比例小，预计明确抑制特征激活是T o进一步探索模型代表性能力。对于更有效的特征提取，还用于直接从输入图像中提取浅特征。典型MR图像上的扩展实验表明我们的横向抑制网络（LIN）实现了更好的SR性能。其他轻量级型号，具有类似的模型规模。

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  • 来源
    《Computer vision and image understanding》 |2020年第12期|103075.1-103075.9|共9页
  • 作者

    Xiaole Zhao; Xiafei Hu; Ying Liao; Tian He; Tao Zhang; Xueming Zou; Jinsha Tian;

  • 作者单位

    School of Computer Science and Technology Southwest University of Science and Technology Mianyang China;

    School of Life Science and Technology University of Electronic Science and Technology of China (UESTC) Chengdu China;

    School of Life Science and Technology University of Electronic Science and Technology of China (UESTC) Chengdu China;

    School of Life Science and Technology University of Electronic Science and Technology of China (UESTC) Chengdu China;

    School of Life Science and Technology University of Electronic Science and Technology of China (UESTC) Chengdu China High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan Chengdu China Key Laboratory for NeuroInformation of Ministry of Education Chengdu China;

    School of Life Science and Technology University of Electronic Science and Technology of China (UESTC) Chengdu China;

    School of Computer Science and Technology Southwest University of Science and Technology Mianyang China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Convolutional neural network; Deep learning; Lateral inhibition; Magnetic resonance imaging; Super-resolution;

    机译：卷积神经网络;深度学习;横向抑制;磁共振成像;超级分辨率;

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