Surface anomaly detection aims to localize abnormal regions in images. A representative approach is the reconstruction-based method, which detect defects via reconstruction errors using generative models trained on normal images. However, these methods cannot directly estimate local distortion levels, which are commonly used for products made of metal or resin. To address this issue, we propose DiffuDewarp, a novel method that directly estimates local distortions. Our approach defines a pseudo-deformation defect generation process as a new diffusion process based on localized warping. Experiments on the MVTec dataset demonstrate that our method outperforms state-of-the-art techniques in categories where local deformations are the main cause of defects. We will release our code upon acceptance.