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Single-pixel compressive imaging based on random DoG filtering
Signal Processing ( IF 4.4 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.sigpro.2020.107746
Maryam Abedi , Bing Sun , Zheng Zheng

Abstract As its name implies, compressive sensing aims to bring compression during sampling. However, the deployment of this technique depends on recovering a high fidelity image through a low number of measurements with a simple hardware and fast software. To this end, we introduce an encoding scheme that by filtering the scene acquires information about the image structure. To prepare a set of proposed encoding patterns, at the first step, a filter bank containing a number of Difference of Gaussian (DoG) kernels with different scales is prepared. Then, by randomly selecting the filters from the bank and under-sampling the scene with them at random points, each encoding pattern is constructed. The idea is inspired by the Human Visual System (HVS) that uses a set of size-tuned DoG kernels at each point in the field-of-view. These encoding patterns, which make a set of linearly independent vectors, form the rows of a structured measurement matrix. This matrix allows making relatively well-conditioned dictionaries by different sparsifying bases. The effectiveness of this method is confirmed by simulations and analyses.

中文翻译:

基于随机DoG滤波的单像素压缩成像

摘要 顾名思义,压缩感知旨在在采样过程中进行压缩。然而,该技术的部署依赖于使用简单的硬件和快速的软件通过少量的测量来恢复高保真图像。为此,我们引入了一种编码方案,通过对场景进行过滤来获取有关图像结构的信息。为了准备一组建议的编码模式,第一步,准备一个包含多个不同尺度的高斯差分 (DoG) 内核的滤波器组。然后,通过从库中随机选择滤波器并在随机点对场景进行欠采样,构建每个编码模式。这个想法受到人类视觉系统 (HVS) 的启发,该系统在视野中的每个点使用一组大小调整的 DoG 内核。这些编码模式,它们构成一组线性无关的向量,形成结构化测量矩阵的行。该矩阵允许通过不同的稀疏基来制作条件相对较好的字典。仿真和分析证实了该方法的有效性。
更新日期:2021-01-01
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