Surface anomaly detection is a vital component in manufacturing inspection. Reconstructive anomaly detection methods restore the normal appearance of an object, ideally modifying only the anomalous regions. Due to the limitations of commonly used reconstruction architectures, the produced reconstructions are often poor and either still contain anomalies or lack details in anomaly-free regions. Recent reconstructive methods adopt diffusion models, however with the standard diffusion process the problems are not adequately addressed. We propose a novel transparency-based diffusion process, where the transparency of anomalous regions is progressively increased, restoring their normal appearance accurately and maintaining the appearance of anomaly-free regions without loss of detail. We propose TRANSparency DifFUSION (TransFusion), a discriminative anomaly detection method that implements the proposed diffusion process, enabling accurate downstream anomaly detection. TransFusion achieves state-of-the-art performance on both the VisA and the MVTec AD datasets, with an image-level AUROC of 98.5% and 99.2%, respectively.

翻译：表面异常检测是制造检验中的一个关键组成部分。重构式异常检测方法旨在恢复物体的正常外观，理想情况下仅修改异常区域。由于常用重构架构的局限性，生成的重构结果往往质量较差，要么仍包含异常，要么在无异常区域缺乏细节。近期的重构方法采用了扩散模型，然而标准扩散过程并未充分解决这些问题。我们提出了一种新颖的基于透明度的扩散过程，其中异常区域的透明度逐渐增加，从而精确恢复其正常外观，同时保持无异常区域外观的细节不丢失。我们提出了TRANSparency DifFUSION（TransFusion），这是一种判别式异常检测方法，实现了所提出的扩散过程，从而支持精确的下游异常检测。TransFusion在VisA和MVTec AD数据集上均取得了最先进的性能，图像级AUROC分别达到98.5%和99.2%。