The limits of AI image enhancement quantifying hallucinated information and scientific fidelity in reconstructed images

Artificially enhanced images generated through learning-based restoration techniques increasingly challenge the assumption that improved visual quality corresponds to preserved physical information. This work investigates radiometric consistency as a measurable criterion for evaluating AI enhanced imaging systems beyond perceptual similarity. A framework is introduced to quantify intensity deviations, structural alterations, and hallucinated features produced during image restoration while maintaining traceability to original acquisition data. Using controlled imaging experiments across degraded and restored datasets, the study analyses how generative models modify signal distributions, contrast relationships, and fine-scale details that influence scientific interpretation. The proposed evaluation approach combines radiometric measurements with image fidelity assessment to distinguish legitimate recovery from synthetic information creation. Results demonstrate that AI enhancement can improve visibility while simultaneously introducing non-physical variations if radiometric constraints are neglected. The research establishes a foundation for reliable AI-assisted image restoration in scientific imaging applications by prioritizing physical accuracy over appearance alone. This contribution supports future development of transparent enhancement methods for imaging systems requiring quantitative validation and preservation of recorded information through rigorous validation of AI restoration pipelines across diverse scientific imaging environments.