Existing multimodal-based image anomaly detection methods suffer from several limitations: anomaly smoothing during anomaly region extraction, insufficient fine-grained perception, and low discrimination efficiency in defect detection, leading to degraded overall performance. To address these issues, this study proposes a multimodal image anomaly detection model with an asymmetric teacher-student network (MATS), comprising three key components: a cross-modal anomaly amplifier (CAA), a multi-dilated local attention (MDLA) module, and a FastKAN feed-forward network. First, the CAA amplifies anomalous regions while reducing noise by expanding/compressing auxiliary features and fusing them with target features, thus alleviating anomaly smoothing in subsequent detection. Subsequently, the MDLA module enhances fine-grained perception of anomalies through multi-dilation-rate convolutions combined with local attention for multi-scale feature extraction, while integrating normalizing flow (NF) to generate the conditional probability distribution of normal samples. The FastKAN module enables efficient anomaly discrimination via lightweight feature processing, producing feature maps consistent with the teacher network’s outputs for pixel-wise distance calculation to evaluate anomaly scores. During testing, regions with significant discrepancies between teacher and student network outputs are identified as anomalies. Experimental results on public industrial image datasets MVTec AD and MVTec 3D-AD demonstrate that the proposed method achieves state-of-the-art performance in multimodal anomaly detection and localization.