Information theory, which describes the transmission of signals in the
presence of noise, has enabled the development of reliable communication
systems that underlie the modern world. Imaging systems can also be viewed as a
form of communication, in which information about the object is "transmitted"
through images. However, the application of information theory to imaging
systems has been limited by the challenges of accounting for their physical
constraints. Here, we introduce a framework that addresses these limitations by
modeling the probabilistic relationship between objects and their measurements.
Using this framework, we develop a method to estimate information using only a
dataset of noisy measurements, without making any assumptions about the image
formation process. We demonstrate that these estimates comprehensively quantify
measurement quality across a diverse range of imaging systems and applications.
Furthermore, we introduce Information-Driven Encoder Analysis Learning (IDEAL),
a technique to optimize the design of imaging hardware for maximum information
capture. This work provides new insights into the fundamental performance
limits of imaging systems and offers powerful new tools for their analysis and
design.

应用信息理论于成像系统的研究中，通过建模对象和测量之间的概率关系，利用只有噪声测量数据的方法估计信息，进而综合量化测量质量，优化成像硬件设计，探索成像系统的基本性能极限，并提供分析和设计工具。