  Detecting anomalous regions in images is a frequently encountered problem in
industrial monitoring. A relevant example is the analysis of tissues and other
products that in normal conditions conform to a specific texture, while defects
introduce changes in the normal pattern. We address the anomaly detection
problem by training a deep autoencoder, and we show that adopting a loss
function based on Complex Wavelet Structural Similarity (CW-SSIM) yields
superior detection performance on this type of images compared to traditional
autoencoder loss functions. Our experiments on well-known anomaly detection
benchmarks show that a simple model trained with this loss function can achieve
comparable or superior performance to state-of-the-art methods leveraging
deeper, larger and more computationally demanding neural networks.
