Ranking Image Fusion the Way Humans Do: A Learned Pairwise Preference Metric for Infrared-Visible Fusion Assessment

The paper introduces LPIFM, a source-conditioned metric for infrared-visible image fusion that jointly examines the infrared source, visible source, and two fused candidates. It predicts whether A is preferred, B is preferred, or the results are perceptually tied. Training uses a newly collected dense preference corpus covering every unordered comparison among a pool of fusion methods on a public benchmark, with blinded labeling and expert adjudication. The authors report strong agreement with human pairwise judgments and tie-aware Bradley-Terry rankings, and state that the dataset, model weights, source code, and evaluation code will be released.