Beyond Resolution and SNR: Measuring What Matters in Imaging

Introduction: The Hidden Information in Imaging Systems

Modern imaging systems often produce data that humans never see directly. Your smartphone processes raw sensor data through complex algorithms before delivering the final photo. MRI scanners collect frequency-space measurements that must be reconstructed before a radiologist can interpret them. Self-driving cars rely on camera and LiDAR data fed straight into neural networks. What matters in all these cases is not how the measurements look—but how much useful information they contain. Artificial intelligence can extract this information even when it's encoded in ways that are unintelligible to human eyes.

Yet, we rarely evaluate imaging systems based on information content directly. Traditional metrics like resolution and signal-to-noise ratio assess individual aspects of quality separately. This makes it hard to compare systems that trade off between these factors. The common alternative—training neural networks to reconstruct or classify images—conflates the quality of the hardware with the quality of the algorithm. We need a better way.

To address this, we developed a framework that enables direct evaluation and optimization of imaging systems based on their information content. In our NeurIPS 2025 paper, we show that this information metric predicts system performance across four imaging domains. Moreover, optimizing for information produces designs that match state-of-the-art end-to-end methods while requiring less memory, less compute, and no task-specific decoder design.

Why Mutual Information?

Mutual information quantifies how much a measurement reduces uncertainty about the object that produced it. Two systems with the same mutual information are equivalent in their ability to distinguish objects—even if their measurements look completely different. This single number captures the combined effect of resolution, noise, sampling, and all other factors that affect measurement quality. For example, a blurry, noisy image that preserves the features needed to distinguish objects can contain more information than a sharp, clean image that loses those features.

Information unifies traditionally separate quality metrics. It accounts for noise, resolution, and spectral sensitivity together rather than treating them as independent factors. Previous attempts to apply information theory to imaging faced two problems:

• The first approach treated imaging systems as unconstrained communication channels, ignoring the physical limitations of lenses and sensors. This produced wildly inaccurate estimates.
• The second approach required explicit models of the objects being imaged, limiting generality.

Our method avoids both pitfalls by estimating information directly from measurements.

Estimating Information from Measurements

Estimating mutual information between high-dimensional variables is notoriously challenging. In our framework, an encoder (the optical system) maps objects to noiseless images, which noise then corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well the measurements distinguish objects. This approach sidesteps the need for a full channel model or a prior distribution over objects, making it both practical and general.

The key insight is that we can evaluate the information content of a system solely from the measurements it produces and the known noise characteristics. This allows us to compare different optical designs—different lenses, different sensor configurations, different trade-offs—on a common, fundamental scale. No need to build a custom decoder or reconstruction algorithm for each candidate design.

Advantages of This Information-Driven Framework

Our method provides several benefits over traditional evaluation approaches:

1. Directness: It computes a single number that captures all relevant quality factors, from resolution to noise to spectral sensitivity.
2. Generality: It works across different imaging domains without requiring domain-specific object models or task-specific decoders.
3. Efficiency: Optimizing for information content uses less memory and compute than end-to-end training of a joint optical-algorithm system.
4. Predictive power: The information metric correlates strongly with final task performance, as we demonstrated in our NeurIPS 2025 paper.

Applications and Results Across Four Imaging Domains

We tested our framework in four distinct imaging domains: microscopy, photography, medical imaging, and autonomous vehicle sensing. In each case, we compared systems designed using our information metric against those designed using end-to-end optimization. The information-optimized designs matched or exceeded the performance of end-to-end systems, but without the need for task-specific algorithm training.

For example, in microscopy, maximizing mutual information led to optical configurations that retained critical cellular details while suppressing noise. In photography, the metric guided trade-offs between aperture size and sensor sensitivity to maximize discriminability of subjects. In medical imaging, it helped select sampling strategies that preserved diagnostic features. And for autonomous sensing, it identified LiDAR patterns that provided the most useful data for neural network processing.

Conclusion: A New Standard for Imaging System Design

Information-driven design offers a principled way to evaluate and optimize imaging systems. By focusing on mutual information between objects and measurements, we can make decisions that directly impact the system's ability to support downstream tasks—whether those tasks are performed by humans or AI. Our framework is efficient, general, and has been validated across multiple domains. We believe it will become a standard tool for engineers and researchers who design the next generation of imaging systems.

For more details, see our NeurIPS 2025 paper: Information-Driven Design of Imaging Systems. You can also explore how to apply this method to your own systems by starting with our open-source estimator.