Abstract

Motivation: Conventional deep learning-based harmonization cannot handle the unseen source domain image when there is no large-size data. Goal(s): We propose blind harmonization, which requires only target domain data during training and generalizes well on unseen source domain data. Approach: BlindHarmony utilizes an unconditional flow model to measure the probability of the target domain image and find a harmonized image that is structurally close to this source domain image but has a high probability in the target domain. Results: BlindHarmony successfully harmonized the source domain image to the target domain and improved the performance of downstream tasks for the data with a domain gap. Impact: Deep learning-based harmonization typically necessitates both source and target domain data, limiting its widespread applicability. This study eliminates the need for source domain data and exhibits robust generalization to new source domain data, thereby expanding the utility of harmonization.