Most modern imaging systems process the data they capture algorithmically before-or instead of-human viewing. As a result, performance depends not on how interpretable the measurements appear, but how effectively they encode details for algorithmic processing. Information theory provides mathematical tools to analyze this, but developing methods that can handle the complexity of real-world measurements yet remain practical enough for widespread use has proven challenging. We introduce a data-driven approach for estimating the information content of imaging system measurements. Our framework requires only experimental measurements and noise characterization, with no need for ground truth data. We demonstrate that these information estimates reliably predict system performance across diverse imaging modalities, including color photography, radio astronomy, lensless imaging, and label-free microscopy. To automate the process of designing imaging systems that maximize information capture we introduce an optimization technique called Information-Driven Encoder Analysis Learning (IDEAL). The tools we develop in this work unlock information theory as a powerful, practical tool for analyzing and designing imaging systems across a broad range of applications. A video summarizing this work can be found at https://waller-lab.github.io/EncodingInformationWebsite/

翻译：大多数现代成像系统在数据被人类观察之前或替代人类观察时，会先通过算法处理所捕获的数据。因此，系统性能不再取决于测量结果的可解释性，而取决于它们对算法处理细节的编码效率。信息论为此提供了数学分析工具，但开发能够处理真实世界测量复杂性、同时又足够实用以广泛使用的方法一直颇具挑战。我们提出了一种数据驱动的方法，用于估计成像系统测量中的信息含量。我们的框架仅需实验测量数据和噪声表征，无需真实数据。我们证明，这些信息估计能够可靠地预测多种成像模态下的系统性能，包括彩色摄影、射电天文学、无透镜成像和无标记显微镜。为了自动化设计能够最大化信息捕获的成像系统，我们引入了一种称为信息驱动编码器分析学习（IDEAL）的优化技术。本工作开发的工具解锁了信息论，使其成为跨广泛应用领域分析和设计成像系统的强大实用工具。总结本工作的视频可在 https://waller-lab.github.io/EncodingInformationWebsite/ 找到。