In this paper, we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in CS CMOS image sensors are recursive pseudo-random binary matrices. We have proved that the restricted isometry property of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random number generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behavior that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a block-based CS smoothed-projected Landweber reconstruction algorithm. By means of single value decomposition, we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach.

中文翻译：

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使用符合 RIP 标准的 CMOS 成像器架构和 Landweber 重建的压缩成像

在本文中，我们提出了一种新的图像传感器架构，用于对自然图像进行快速准确的压缩感知 (CS)。CS CMOS 图像传感器中通常采用的测量矩阵是递归伪随机二进制矩阵。我们已经证明这些矩阵的受限等距特性受到低稀疏常数的限制。这些矩阵的质量还受到伪随机数生成器 (PRNG) 的非理想性的影响。为了克服这些限制，我们提出了一种硬件友好的伪随机三元测量矩阵，通过 III 类基本元胞自动机 (ECA) 在片上生成。这些 ECA 呈现出一种比其他 PRNG 更好地模拟随机 CS 测量矩阵的混沌行为。我们将这种新架构与基于块的 CS 平滑投影 Landweber 重建算法相结合。通过单值分解，我们调整了该算法以在处理二元和三元矩阵时执行快速而精确的重建。提供了模拟来验证该方法。