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Scalable Deep Compressive Sensing
arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2021-01-20 , DOI: arxiv-2101.08024
Zhonghao Zhang, Yipeng Liu, Xingyu Cao, Fei Wen, Ce Zhu

Deep learning has been used to image compressive sensing (CS) for enhanced reconstruction performance. However, most existing deep learning methods train different models for different subsampling ratios, which brings additional hardware burden. In this paper, we develop a general framework named scalable deep compressive sensing (SDCS) for the scalable sampling and reconstruction (SSR) of all existing end-to-end-trained models. In the proposed way, images are measured and initialized linearly. Two sampling masks are introduced to flexibly control the subsampling ratios used in sampling and reconstruction, respectively. To make the reconstruction model adapt to any subsampling ratio, a training strategy dubbed scalable training is developed. In scalable training, the model is trained with the sampling matrix and the initialization matrix at various subsampling ratios by integrating different sampling matrix masks. Experimental results show that models with SDCS can achieve SSR without changing their structure while maintaining good performance, and SDCS outperforms other SSR methods.

中文翻译:

可扩展的深度压缩感测

深度学习已用于图像压缩感测(CS),以增强重建性能。但是,大多数现有的深度学习方法针对不同的子采样率训练不同的模型,这带来了额外的硬件负担。在本文中,我们为所有现有的端到端训练模型的可扩展采样和重构(SSR)开发了一个名为可扩展深度压缩感知(SDCS)的通用框架。以提出的方式,对图像进行线性测量和初始化。引入了两个采样掩码以分别灵活控制分别用于采样和重构的子采样率。为了使重建模型适应任何子采样率,开发了一种称为可扩展训练的训练策略。在可扩展的培训中,通过集成不同的采样矩阵掩码,以不同的子采样率使用采样矩阵和初始化矩阵对模型进行训练。实验结果表明,具有SDCS的模型可以在不更改结构的情况下实现SSR,同时保持良好的性能,并且SDCS优于其他SSR方法。
更新日期:2021-01-21
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