Nonuniform sampling with adaptive expectancy based on local variance

W. Guicquero, A. Verdant, A. Dupret, P. Vandergheynst
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引用次数: 5

Abstract

A new trend in sensor array architectures is to provide compact implementations based on alternative acquisitions and sampling. In particular, with the recent rise of Compressive Sensing (CS), multiple sensing schemes have been developed. However, for the moment, CS reconstruction techniques take a relatively long time to properly converge. Therefore, it limits the sensor resolution and potential applications. On the other hand, it generally involves complex structures and circuitries at the sensor side. This work proposes an acquisition chain performing an adaptive sensing of pseudo-randomly selected samples. This specific nonuniform sampling scheme allows to parallelize and simplify the acquisition thanks to a compact design based on sigma delta converters and cellular automata. Previous works show that compared to state of the art and without an important image degradation, dedicated reconstructions to this specific sampling can considerably reduce the overall computation time.
基于局部方差的自适应期望非均匀抽样
传感器阵列架构的一个新趋势是提供基于替代采集和采样的紧凑实现。特别是,随着压缩感知(CS)的兴起,多种感知方案得到了发展。然而,目前,CS重建技术需要较长的时间才能正确收敛。因此,它限制了传感器的分辨率和潜在的应用。另一方面,它通常涉及传感器侧复杂的结构和电路。这项工作提出了一个采集链,执行伪随机选择样本的自适应感知。这种特定的非均匀采样方案允许并行化和简化采集,这要归功于基于σ δ转换器和元胞自动机的紧凑设计。先前的研究表明,与目前的技术水平相比,在没有严重图像退化的情况下,对这种特定采样进行专门的重建可以大大减少总体计算时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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