The advancement of new digital image sensors has enabled the design of exposure multiplexing schemes where a single image capture can have multiple exposures and conversion gains in an interlaced format, similar to that of a Bayer color filter array. In this paper, we ask the question of how to design such multiplexing schemes for adaptive high-dynamic range (HDR) imaging where the multiplexing scheme can be updated according to the scenes. We present two new findings. (i) We address the problem of design optimality. We show that given a multiplex pattern, the conventional optimality criteria based on the input/output-referred signal-to-noise ratio (SNR) of the independently measured pixels can lead to flawed decisions because it cannot encapsulate the location of the saturated pixels. We overcome the issue by proposing a new concept known as the spatially varying exposure risk (SVE-Risk) which is a pseudo-idealistic quantification of the amount of recoverable pixels. We present an efficient enumeration algorithm to select the optimal multiplex patterns. (ii) We report a design universality observation that the design of the multiplex pattern can be decoupled from the image reconstruction algorithm. This is a significant departure from the recent literature that the multiplex pattern should be jointly optimized with the reconstruction algorithm. Our finding suggests that in the context of exposure multiplexing, an end-to-end training may not be necessary.

翻译：摘要：新型数字图像传感器的进步使得曝光多路复用方案的设计成为可能，其中单次图像捕获可以以交错格式（类似于拜耳颜色滤镜阵列）实现多重曝光和转换增益。本文探讨了如何针对自适应高动态范围成像设计此类多路复用方案，使其可根据场景更新。我们提出两项新发现：（i）解决设计最优性问题。我们证明，给定多路复用模式，基于独立测量像素输入/输出信噪比的传统最优性准则可能因无法包含饱和像素位置而导致错误决策。为解决该问题，我们提出新概念——空间变曝光风险（SVE-Risk），它是一种伪理想化可恢复像素数量的量化指标，并给出高效枚举算法以选择最优多路复用模式。（ii）报告设计普适性观察，即多路复用模式的设计可与图像重建算法解耦。这显著区别于近期文献中多路复用模式应与重建算法联合优化的观点；我们的发现表明，在曝光多路复用场景中，端到端训练可能并非必要。