Rethinking the concept of pixel intensity contrast from a machine learning perspective

Abstract

Image contrast is a critical factor for machine vision tasks. A promising approach for enhancing contrast involves the use of algorithmically optimized, spectrally tunable illumination. However, the very definition of 'contrast' is often rooted in principles of human perception, which may not be optimal for a machine observer. For an algorithm, contrast is an objective, task-driven metric that can be mathematically defined. To investigate the impact of this definition, we first use eigenvalue-based optimization algorithms to compute optimal illumination spectra. We then systematically evaluate these spectra using four distinct, physically realizable contrast formulations. Our analysis reveals that the performance of a given optimization algorithm is entirely dependent on the subsequent choice of evaluation metric. An illumination spectrum considered optimal under one metric can be significantly suboptimal when measured by another. This demonstrates that the choice of contrast metric is not a passive measurement, but an active design parameter with tangible physical consequences. From a machine learning perspective, the choice of this 'loss function' should be codesigned with the physical hardware and the ultimate downstream task to achieve true system-level optimization.