Modern imaging systems—from smartphone cameras to medical MRI scanners—generate vast amounts of data, but much of that data is never directly interpreted by humans. Instead, algorithms process raw sensor measurements to produce final images or extract actionable insights. The true value of an imaging system lies not in how its measurements appear, but in how much useful information those measurements contain. Yet traditional evaluation methods often overlook this fundamental aspect, leading to suboptimal designs. A new framework, presented at NeurIPS 2025, offers a direct way to assess and optimize imaging systems based on their information content, promising more efficient and effective hardware-software co-design.
The Problem with Traditional Metrics
Conventional imaging quality metrics, such as resolution and signal-to-noise ratio (SNR), evaluate individual aspects of performance in isolation. This piecemeal approach makes it difficult to compare systems that trade off between these factors. For example, a system with higher resolution but lower SNR might be better or worse than one with lower resolution but cleaner images, depending on the intended application. Furthermore, neural network-based approaches that reconstruct or classify images conflate the performance of the imaging hardware with that of the algorithm, obscuring the hardware's true capabilities.
What is needed is a unified metric that captures the combined effect of all relevant factors—resolution, noise, sampling, dynamic range, spectral sensitivity—in a single number. This would enable direct, fair comparisons and guide optimization toward the most informative designs.
Why Mutual Information?
Mutual information provides exactly such a unified measure. It quantifies how much a measurement reduces uncertainty about the object that produced it. Two imaging systems with the same mutual information are equivalent in their ability to distinguish different objects, even if their raw measurements look completely different. A blurry, noisy image that preserves the subtle features needed to differentiate between similar objects can contain more information than a sharp, clean image that inadvertently discards those critical details.
Mutual information inherently accounts for noise, resolution, spectral sampling, and all other factors that affect measurement quality. It treats these interdependent characteristics not as separate variables, but as components of a single information channel. This holistic view aligns with the ultimate goal of any imaging system: to extract the most useful data from the scene.
Previous Limitations and Our Solution
Earlier attempts to apply information theory to imaging design faced two major obstacles. The first approach treated imaging systems as unconstrained communication channels, ignoring the physical limitations of lenses and sensors. This led to wildly inaccurate estimates because, for example, a lens with a finite aperture cannot transmit as much information as a hypothetical lossless channel. The second approach required explicit, often restrictive models of the objects being imaged, limiting generality and practical applicability.
Our method overcomes both limitations by estimating mutual information directly from the noisy measurements themselves. Using only the observed data and a known noise model, our information estimator computes how well the system can distinguish between different objects. This avoids the need for object models and respects the physical constraints of the optical and sensor hardware. The result is a practical, data-driven tool that can evaluate and optimize any imaging system, from a simple camera to a complex multispectral sensor array.
Practical Implications and Results
We validated our framework across four distinct imaging domains: microscopy, remote sensing, medical imaging, and autonomous driving. In each case, the information metric predicted the system's overall performance in downstream tasks (such as classification or reconstruction) more accurately than traditional metrics. Moreover, when we optimized the hardware parameters using our information objective, the resulting designs matched or exceeded the performance of state-of-the-art end-to-end learning approaches—but with significantly lower memory and computational requirements, and without needing to design task-specific decoders.
This efficiency is crucial for real-world applications where computational resources are limited, such as in edge devices for autonomous vehicles or portable medical diagnostic tools. By decoupling hardware optimization from software algorithm design, our approach enables engineers to build better cameras, lenses, and sensors without being constrained by the need to train large neural networks for every design iteration.
Conclusion
Information-driven design represents a paradigm shift in imaging system optimization. Instead of relying on fragmented metrics or black-box end-to-end training, we can now directly and efficiently quantify what matters most: the amount of useful information captured. This framework not only simplifies design workflows but also leads to more optimal systems that make the best use of physical hardware. As imaging continues to evolve toward increasingly algorithmic interpretation, this information-theoretic approach will become indispensable for building the next generation of smart cameras, medical scanners, and autonomous sensors.