The presence of stripe noise in multispectral data is a common issue caused by various factors during the imaging process. This noise severely degrades image quality and imposes limitations on downstream tasks. Although deep learning-based methods have demonstrated promising results in destriping, they often encounter challenges due to the disparity between the stripe noise distribution in simulated and real images. As a result, their destriping performance on real data is significantly hindered. To address this challenge, we propose a semisupervised disentangled transformation network (SDTNet) that encourages the model to learn the real stripe noise distribution through image decoupling and noise transformation using simulated and real data. SDTNet consists of the simulated and real image branches, which are subject to supervised and unsupervised constraints, respectively. They are jointly trained to enhance each other mutually. Furthermore, we introduce a decoupling strategy that effectively preserves the clean background component using self-consistency and adversarial losses. Instead of directly converting the whole image from the simulated domain to the real domain, SDTNet focuses on the relatively simpler task of converting the stripe noise component while maintaining the consistency of the image background. Extensive experimental evaluations on various datasets demonstrate the superior destriping performance of the proposed SDTNet compared to other methods, particularly in effectively removing stripe noise from real images.