深度学习中图像压缩感知的研究进展

Kaiguo Xia, Lei Hu, Pengqiang Mao
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引用次数: 0

摘要

近年来,深度学习在图像识别领域发展迅速,为压缩感知的重构提供了新的思路。基于深度学习的新方法可以通过网络测量测量信号与原始信号之间的相关性,不仅具有较高的重构精度,而且显著减少了耗时,显示了深度学习在压缩感知重构领域的巨大潜力。本文对目前基于深度学习的图像压缩感知重构方法进行了梳理,根据三种不同的深度网络结构,分析了算法的特点和关键步骤,最后展望了基于深度学习的压缩感知重构的发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review of Image Compressed Sensing in Deep Learning
In recent years, deep learning has developed rapidly in the field of image recognition, which provides a new idea for the reconstruction of compressed sensing. The new method based on deep learning can measures the correlation between the measurement signal and the original signal through network , which not only has high reconstruction accuracy, but also significantly reduces the time consuming, showing the great potential of deep learning in the field of compressed sensing reconstruction. This paper sorts out the current image compressed sensing reconstruction methods based on deep learning, analyzes the characteristics and key steps of the algorithm according to three different deep network structures, and finally looks forward to the development direction of compressed sensing reconstruction based on deep learning.
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