From Pixels to Purpose: How Mutual Information Transforms Imaging System Design

Introduction: Rethinking What Matters in Imaging

Modern imaging systems do much more than capture pictures we can admire. Your smartphone’s camera processes raw sensor data through complex algorithms before you ever see the final image. An MRI scanner collects signals in frequency space—a domain entirely unintelligible to the human eye—and then reconstructs it into anatomical slices. Self-driving cars feed camera and LiDAR data directly into neural networks that make split-second navigation decisions.

In all these cases, the usefulness of an imaging system depends not on how its measurements look, but on how much actionable information they contain. Artificial intelligence can extract that information even when it is encoded in ways that humans cannot interpret. Yet, surprisingly, the imaging community rarely evaluates information content directly.

The Blind Spots of Traditional Metrics

Conventional image quality metrics—resolution, signal-to-noise ratio (SNR), dynamic range—assess individual aspects of system performance separately. A sharper image might be noisier; a cleaner image might miss critical spectral features. Because these metrics are disconnected, comparing two systems that trade off between them becomes a messy, ad-hoc process.

The most common alternative is to train a neural network to perform a specific task—such as reconstructing an image or classifying an object—and then judge the hardware based on the network’s performance. But this approach conflates the quality of the imaging hardware with the quality of the algorithm. A mediocre camera paired with a brilliant neural network can outperform an excellent camera with a poor decoder, making it impossible to isolate the contribution of the optics and sensor.

What the field needs is a unified, task-independent metric that captures the full potential of an imaging system—one that reflects how much useful information the system can convey, regardless of how that information will ultimately be used.

Why Mutual Information Is the Answer

Mutual information is a concept from information theory that measures how much one random variable tells us about another. In the context of imaging, it quantifies how much a measurement reduces uncertainty about the object that produced it. Two imaging systems that have the same mutual information are equally capable of distinguishing between different objects, even if their raw measurements look completely different.

This single number elegantly captures the combined effect of resolution, noise, sampling, spectral sensitivity, and all other factors that affect measurement quality. A blurry, noisy image that preserves the features needed to tell two objects apart can contain more mutual information than a sharp, clean image that accidentally discards those same features.

Mutual information thus unifies traditionally separate quality metrics. It treats noise, resolution, and spectral response as interdependent factors—as they are in reality—rather than as independent knobs that can be tuned in isolation.

Why Previous Information-Theoretic Approaches Fell Short

Information theory has been applied to imaging before, but earlier attempts stumbled on two fundamental obstacles:

• Unconstrained channel model: Some researchers treated imaging systems as if they were ideal communication channels, ignoring the physical limitations of lenses and sensors. This led to wildly inaccurate information estimates.
• Explicit object models: Others required a detailed, handcrafted model of the objects being imaged. This limited generality and made the approach impractical for real-world systems where object distributions are complex and unknown.

Our framework avoids both problems by estimating mutual information directly from measurements, using only the noisy data and a stochastic noise model. No explicit object model is needed, and physical constraints are respected.

Our Approach: Estimating Information from Measurements

Estimating mutual information between high-dimensional variables (such as an object scene and its measurement) is notoriously difficult. We developed a technique that leverages the structure of the imaging pipeline. Starting from a set of noisy measurements and a known noise model, we compute an upper bound on the mutual information that the measurements provide about the object. This bound is tight enough to serve as a reliable figure of merit.

Key advantages of our method:

• No task-specific decoder needed: The metric is independent of any particular algorithm that might later process the measurements. It evaluates the hardware alone.
• Low memory and compute: Because the estimation does not require training a neural network or running multiple reconstruction passes, it is far more efficient than end-to-end optimization of optics-plus-algorithm pipelines.
• Generality across domains: In our NeurIPS 2025 paper, we demonstrate that the information metric predicts system performance across four different imaging domains: visible-light cameras, infrared sensors, computed tomography, and phase-contrast microscopy.

Results: Information-Driven Optimization at Scale

We used the mutual information metric as an objective function to optimize imaging system designs—for example, choosing the shape of a lens or the sampling pattern of a sensor. The resulting designs matched or outperformed those produced by state-of-the-art end-to-end learning methods that simultaneously optimize hardware and a downstream neural network, but with far less computational cost and without needing to design the decoder.

This demonstrates that information-driven design is not just a theoretical curiosity: it is a practical tool that can accelerate the development of better imaging systems, from medical scanners to autonomous vehicle sensors.

Conclusion: A New Lens for Imaging System Design

The shift from pixel-centric metrics to information-centric evaluation is long overdue. By focusing on how much useful information an imaging system can deliver, we can directly compare radically different hardware configurations and optimize them without conflating the roles of optics and algorithms. Our framework provides a scalable, practical way to do this—opening the door to imaging systems that are designed for their true purpose: conveying information.

For more details, see our full paper Information-Driven Design of Imaging Systems (NeurIPS 2025).