AFSC: Adaptive Fourier Space Compression for Anomaly Detection

The primary challenge faced by reconstruction-based anomaly detection (AD) methods is that neural networks exhibit strong generalization, resulting in a high probability and accuracy of anomaly reconstruction. Several existing methods attempt to alleviate this problem by randomly masking partial image regions and reconstructing the image from partial inpaintings. However, local masking in spatial space is not guaranteed to remove anomalous regions during the testing phase and poses the risk of normal regions being inaccurately reconstructed. Hence, we explore an approach to compress the global information of the image while ensuring the loss of partial anomaly information renders it difficult to reconstruct. Inspired by the fact that each Fourier coefficient contains global information of the image, we propose an adaptive Fourier space compression (AFSC) method. Specifically, the Fourier coefficients of the input image are sparsely sampled by binary masks obtained from the AFSC module (AFSCm). In AFSCm, the masks are jointly optimized with the reconstruction network subject to sparsity constraint. The learned masks are forced to selectively retain part of the global information that is favourable to recovering normal images. In addition, we introduce an efficient Fourier convolution module that enables the network to accurately reconstruct normal regions under conditions of losing partial information. Experimental results on three benchmarks of industrial scenarios demonstrate our method (without external prior) achieves competitive results compared with recent methods.