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An Algorithm Architecture Co-Design for CMOS Compressive High Dynamic Range Imaging

机译:CMOS压缩高动态范围成像的算法架构协同设计

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摘要

Standard image sensors feature dynamic range about 60 to 70 dB while the light flux of natural scenes may be over 120 dB. Most imagers dedicated to address such dynamic ranges, need specific, and large pixels. However, canonical imagers can be used for high dynamic range (HDR) by performing multicapture acquisitions to compensate saturation. This technique is made possible at the expense of the need for large memory requirements and an increase of the overall acquisition time. On the other hand, the implementation of compressive sensing (CS) raises the same issues regarding the modifications of both the pixel and the readout circuitry. Assuming HDR images are sufficiently sparse, CS claims they can be reconstructed from few random linear measurements. A novel CS-based image sensor design is presented in this paper allowing a compressive acquisition without changing the classical pixel design, as well as the overall sensor architecture. In addition to regular CS, HDR CS is enabled thanks to specific time diagrams of the control signals. An alternative nondestructive column-based readout mode constitutes the main change compared to a traditional functioning. The HDR reconstruction, which is also presented in this paper, is based on merging the information of multicapture compressed measurements while taking into account noise sources and nonlinearities introduced by both the proposed acquisition scheme and its practical implementation.
机译:标准图像传感器的动态范围约为60至70dB,而自然场景的光通量可能超过120dB。大多数致力于解决此类动态范围的成像器需要特定的大像素。但是,通过执行多捕获采集以补偿饱和度,可以将规范成像器用于高动态范围(HDR)。使这种技术成为可能,但需要大的存储需求并增加总的采集时间。另一方面,压缩感测(CS)的实现提出了有关像素和读出电路的修改的相同问题。假设HDR图像足够稀疏,CS声称可以通过很少的随机线性测量来重建它们。本文提出了一种新颖的基于CS的图像传感器设计,可以在不更改经典像素设计以及整体传感器架构的情况下进行压缩采集。除了常规CS外,还可以通过控制信号的特定时序图来启用HDR CS。与传统功能相比,替代性的非破坏性基于列的读取模式构成了主要变化。本文还介绍了HDR重建,该重建基于合并多捕获压缩测量的信息,同时考虑了所提出的采集方案及其实际实现所引入的噪声源和非线性。

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