Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing.

中文翻译：

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自适应压缩感知：一款值得玩的游戏

尽管压缩感知机制具有普遍性，但关于要获取的稀疏信号类别的附加信息允许进行调整，从而产生实质性的改进。事实上，适当利用这些先验可以显着增加给定重建质量的压缩。由于压缩感知最有前途的应用范围之一是受极低资源约束的物联网设备，因此当它可以应对与硬件相关的约束并允许低复杂性实现时，适应性尤其有趣。我们在这里回顾和比较了许多算法适应策略，这些策略侧重于压缩感知的编码部分或恢复部分。我们还回顾了其他更面向硬件的适配技术，这些技术实际上能够在实际实现中发挥作用。在所有情况下，适应性被证明是一种应该在实际应用中掌握以释放压缩感知全部潜力的工具。