This study focuses on image style transfer techniques and presents improvements to the ArtFlow framework proposed by Jie An et al.. ArtFlow employs a reversible mechanism that maps an image from the pixel space to the feature space during the forward process, transforming it into a feature vector. A style transfer module then converts this feature vector into a stylized one. In the reverse process, the stylized feature vector is mapped back to the pixel space to obtain the final stylized image, effectively preserving the original content details. This reversible design helps prevent content leakage, a common issue in traditional methods. However, there remains room for improvement in terms of adaptability. To enhance the model's adaptability while maintaining structural fidelity, this paper modifies the core activation normalization mechanism within the ArtFlow framework and proposes a novel normalization approach. Inspired by the concept of "learning to learn" in meta-learning, we introduce Meta Activation Normalization (Meta-Actnorm). The improved architecture is termed the Multi-Block Adaptive Flow (MBAF) model. In the MBAF model, Meta-Actnorm dynamically adjusts normalization parameters based on the input image during the forward process and effectively integrates these parameters during the reverse process, further enhancing the model’s adaptability and stability. A series of experiments and quantitative evaluations demonstrate that the proposed method not only preserves key structures and details of the content image but also ensures visual consistency after style transfer, avoiding distortions such as structural deformation.
