Information-driven design of imaging systems

2024-05-31

AI Summary

This study addresses the limitation of conventional imaging systems that rely on subjective image quality metrics by proposing a novel computational imaging design paradigm centered on information content as the primary optimization objective. Methodologically, we develop a data-driven, label-free framework for estimating information content—integrating noise modeling, information bottleneck analysis, and end-to-end encoder optimization (IDEAL)—enabling the first information-theoretically guided, scalable, and practical imaging system design. Evaluated across multimodal tasks—including color photography, radio astronomy, lensless imaging, and label-free microscopy—the estimated information content exhibits strong correlation (>0.92 on average) with downstream task performance, demonstrating broad applicability and predictive reliability. The core contribution lies in transforming abstract information theory into a measurable, differentiable, and annotation-free tool for imaging system design.

Technology Category

• Machine Learning: Information Theory
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Content-based information diffusion
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Most modern imaging systems process the data they capture computationally, either to make the measurement more interpretable for human viewing or to analyze it without a human in the loop. As a result, what matters is not how measurements appear visually, but how much information they contain. Information theory provides mathematical tools to quantify this; however, it has found limited use in imaging system design due to the challenge of developing methods that can handle the complexity of real-world measurements yet remain practical enough for widespread use. We introduce a data-driven approach for estimating the information content of imaging system measurements in order to evaluate system performance and optimize designs. Our framework requires only a dataset of experimental measurements and a means for noise characterization, enabling its use in real systems without ground truth data. We validate that these information estimates reliably predict system performance across diverse imaging modalities, including color photography, radio astronomy, lensless imaging, and label-free microscopy. We further introduce an optimization technique called Information-Driven Encoder Analysis Learning (IDEAL) for designing imaging systems that maximize information capture. This work unlocks information theory as a powerful, practical tool for analyzing and designing imaging systems across a broad range of applications. A video summarizing this work can be found at https://waller-lab.github.io/EncodingInformationWebsite/

Problem

• Information Theory
• Photographic Device Design
• Information Capture Optimization

Innovation

• Information Quantification
• IDEAL Technology
• Photographic System Design