Convolutional autoencoders have emerged as popular methods for unsupervised
defect segmentation on image data. Most commonly, this task is performed by
thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance.
This procedure, however, leads to large residuals whenever the reconstruction
encompasses slight localization inaccuracies around edges. It also fails to
reveal defective regions that have been visually altered when intensity values
stay roughly consistent. We show that these problems prevent these approaches
from being applied to complex real-world scenarios and that it cannot be easily
avoided by employing more elaborate architectures such as variational or
feature matching autoencoders. We propose to use a perceptual loss function
based on structural similarity which examines inter-dependencies between local
image regions, taking into account luminance, contrast and structural
information, instead of simply comparing single pixel values. It achieves
significant performance gains on a challenging real-world dataset of
nanofibrous materials and a novel dataset of two woven fabrics over the state
of the art approaches for unsupervised defect segmentation that use pixel-wise
reconstruction error metrics.

本文介绍了利用结构相似性的感知损失函数，通过考虑图像之间的相互依赖关系，包括亮度，对比度和结构信息，而不是简单的单像素值对比，来实现对复杂真实数据集的未监督缺陷分割，通过对仿生材料和织物数据集的实际应用表明，该方法在性能上显著优于以像素为基础重建误差度量的非网络方法以及先前的优化卷积自动编码器。