Beyond Human Vision: Designing Imaging Systems by Information Content

Introduction

Modern imaging systems often produce raw data that humans never see directly. Smartphones run complex algorithms on sensor readings before we ever see a picture. MRI scanners collect frequency-space measurements that require reconstruction for a doctor to interpret. Autonomous vehicles feed camera and LiDAR streams straight into neural networks, bypassing any visual display.

What matters in all these cases is not the appearance of the measurements, but the information they carry. An AI system can extract actionable data even when encoded in a form unintelligible to a person. Yet until recently, we lacked a direct way to evaluate or optimize imaging hardware based on information content.

The Problem with Traditional Metrics

Conventional imaging benchmarks treat resolution, signal-to-noise ratio, and dynamic range as separate factors. This makes it difficult to compare systems that trade off between them—for example, a sharp but noisy image versus a blurry but clean one. Engineers must rely on ad hoc combinations or subjective assessments.

The most common alternative is to train neural networks to perform a specific task, such as reconstruction or classification. However, this approach conflates hardware quality with algorithm quality. A better lens might appear inferior simply because the accompanying software is poorly trained; conversely, clever algorithms can mask mediocre optics. We need a metric that isolates the hardware's intrinsic ability to carry information.

Mutual Information as a Unified Metric

Mutual information measures how much a measurement reduces uncertainty about the object that produced it. If two systems yield the same mutual information, they are equally capable of distinguishing objects—even if their raw measurements look completely different.

This single number captures the combined effect of resolution, noise, sampling, and spectral sensitivity. It unifies formerly separate quality axes into one principled metric. For instance, a blurry, noisy image that preserves the features needed for discrimination can contain more information than a sharp, clean image that accidentally discards those features.

Direct Estimation from Measurements

Earlier attempts to apply information theory to optical design faced two obstacles. The first treated the imaging system as an unconstrained communication channel, ignoring physical limits like lens diffraction and sensor saturation—leading to wildly inaccurate estimates. The second required a precise probabilistic model of the objects being imaged, which is rarely available in practice.

Our method, presented at NeurIPS 2025, sidesteps both problems. It estimates mutual information directly from noisy measurements and a known noise model. No explicit object model is needed; the algorithm learns the relevant structure from data. This makes the approach general across different imaging modalities—optical microscopy, X-ray CT, radar, and more.

Practical Applications and Results

Four Imaging Domains Validated

We tested the information metric across four distinct imaging tasks: classification, segmentation, super‑resolution, and compressive sensing. In every case, the information number predicted actual task performance—systems with higher mutual information consistently yielded better results from downstream algorithms.

Optimization Without Task‑Specific Decoders

When we optimized hardware designs to maximize mutual information, the resulting systems matched or exceeded state‑of‑the‑art end‑to‑end learned designs. Crucially, our approach required no task‑specific decoder during optimization, reducing memory and compute requirements. Engineers can now co‑design optics and sensors with information as the guiding objective, without needing to train a separate neural network for each target task.

Conclusion

By reframing imaging system design around information content, we move beyond legacy metrics like resolution and SNR. Mutual information provides a unified, physically grounded objective that directly correlates with real‑world performance. The ability to estimate it from measurements alone opens the door to automated design pipelines for cameras, medical scanners, and autonomous sensors—systems that are optimized for information, not for human eyes.