Measuring What Matters: Information-Driven Design for Next-Generation Imaging Systems

Introduction

Modern imaging systems often generate measurements that humans never see directly. Your smartphone's camera converts raw sensor data into a polished photo through complex algorithms. An MRI scanner acquires frequency-domain signals, which must be reconstructed before a radiologist can interpret them. Self-driving cars feed camera and LiDAR data directly into neural networks without any human-visible intermediate. In all these cases, the true value of the measurements lies not in their appearance but in the information they carry. Artificial intelligence can extract that information even from data that looks like gibberish to a human observer.

Why Traditional Metrics Fall Short

Despite this reality, engineers still rely on conventional performance metrics such as resolution, signal-to-noise ratio, and modulation transfer function. These metrics assess individual aspects of image quality in isolation, making it nearly impossible to compare systems that trade off, say, sharpness for lower noise or higher dynamic range. A more fundamental problem: when researchers train neural networks to reconstruct or classify images, the measured performance conflates the quality of the imaging hardware with the quality of the algorithm. You cannot tell whether a poor result stems from the optics, the sensor, or the processing pipeline.

Mutual Information: The Unifying Metric

Mutual information quantifies how much a measurement reduces uncertainty about the object that produced it. Two imaging systems with identical mutual information are equally capable of distinguishing between objects, even if their raw measurements look completely different. This single number captures the combined effect of resolution, noise, sampling efficiency, spectral sensitivity, and every other factor that influences measurement quality. Remarkably, a blurry, noisy image that preserves the features essential for discrimination can contain more information than a sharp, clean image that misses those features.

A Single Number That Binds Them All

Information unifies quality metrics that are usually treated separately. Instead of balancing resolution against noise or spectral coverage against dynamic range, you can optimize directly for the quantity that ultimately matters: the ability to distinguish objects. This is especially powerful in systems where the measurement space is high-dimensional and the final decision is made by a machine classifier.

Overcoming Previous Limitations

Earlier attempts to apply information theory to imaging systems stumbled on two obstacles. The first approach modeled the system as an unconstrained communication channel, ignoring the physical limitations of lenses, sensors, and real-world noise sources. The resulting information estimates were wildly optimistic and practically useless. The second approach required an explicit probabilistic model of the objects being imaged, which severely limited generality and applicability to unknown scenes.

Estimating Information Directly from Measurements

Our framework avoids both problems by estimating mutual information directly from the noisy measurements, using only a statistical noise model. We do not need to model the object distribution, nor do we ignore physical constraints. The estimator takes the raw sensor outputs and a noise model, then computes how much uncertainty about the object remains after observing the measurement. This yields a practical, system-level information metric that can be used for both evaluation and design.

Proven Across Four Imaging Domains

In our NeurIPS 2025 paper (see conclusion), we demonstrate the power of this information metric across four diverse imaging domains: visible-light photography, computed tomography, hyperspectral imaging, and single-pixel cameras. In every domain, the information score strongly correlates with task performance—whether the task is classification, segmentation, or reconstruction. Moreover, when we use the metric as an optimization target for designing new optical systems, the resulting designs match the performance of state-of-the-art end-to-end methods but require less memory, less computation, and no task-specific decoder network.

The Future of Imaging Design

By shifting from indirect quality metrics to direct information estimation, engineers can design imaging systems that are fundamentally optimized for what matters: the ability to extract meaningful information from the world. This approach decouples hardware quality from algorithm performance and provides a single, theoretically grounded number for comparison. As machine perception becomes ubiquitous, information-driven design will become the standard for building the next generation of cameras, sensors, and scientific instruments.

For more details, see our NeurIPS 2025 paper or contact the authors.